Data Collection Practices of Mobile Applications Played by Preschool-Aged Children
Open accessAbstract
Importance: Child-directed mobile applications (apps) have been found to collect digital identifiers and transmit them to third-party companies, a potential violation of federal privacy rules. This study seeks to examine the differences in app data collection and sharing practices by evaluating the sociodemographic characteristics of the children who play them. Objective: To examine data collection and sharing practices of 451 apps played by young children and to test associations with child sociodemographic characteristics. Design, Setting, and Participants: This study used data from the baseline phase of the Preschooler Tablet Study, a prospective cohort study conducted from August 2018 to January 2020. This study used a population-based sample. A convenience sample of the parents of preschool-aged children was recruited from pediatric offices, childcare centers, social media posts, and an online participant registry. Eligibility criteria included (1) parent or guardian of a child aged 3 to 5 years, (2) parent or guardian who lived with the child at least 5 days per week, (3) participants who spoke English, and (4) a child who used an Android (Google LLC) device. All interactions with participants were through email, online surveys, and mobile device sampling. Exposures: Sociodemographic characteristics were assessed by parental report. Main Outcomes and Measures: This study tested the hypothesis that data transmissions to third-party domains are more common in apps played by children from low-socioeconomic-status homes. Child app usage was assessed via a mobile sampling app for an average of 9 days. Persistent identifier data transmissions to third-party domains were quantified for each app using an instrumented Android environment with monitoring of network traffic; for each child, the counts of total data transmissions were calculated, and the total third-party domains were detected for the apps they played. Results: Our sample comprised 124 children who used Android devices (35 tablets, 89 smartphones; 65 girls [52%]; mean [SD] age, 3.85 [0.57] years; 87 non-Hispanic White [71%]). One hundred twenty of participating parents (97%) were women. Of 451 apps tested, 303 (67%) transmitted persistent identifiers to 1 to 33 third-party domains. Child data transmission counts ranged from 0 to 614 (median [interquartile range], 5.0 [1-17.5]) and third-party domain counts from 0 to 399 (4.0 [1-12.5]). In multivariable negative binomial regression models, higher transmission and third-party domain rates per app were positively associated with older age (rate ratio, 1.67 [95% CI, 1.20-2.33]; P = .002 and 1.69 [95% CI, 1.26-2.27]; P < .001, respectively) and lower parent educational attainment (eg, high school or General Educational Development or less rate ratio, 2.29 [95% CI, 1.20-4.39]; P = .003 and 2.05 [95% CI, 1.13-3.70]; P < .02, respectively), but not with household income. Conclusions and Relevance: This study found that apps used by young children had a high frequency of persistent identifier transmissions to third-party companies, suggesting that federal privacy rules are not being enforced. Older children, those with their own devices, or those from lower-education households may be at higher risk of potential privacy violations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A recent study highlights the greater likelihood of digital data being collected and shared from children's use of apps when those children come from lower-education backgrounds.
“For example, a recent study highlights the greater likelihood of digital data being collected and shared from children's use of apps when those children come from lower‐education backgrounds (Zhao et al., 2020).”
From Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative (2021) · cites this paper as Zhao et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Exposure and use of mobile media devices by young children
Kabali, H. K., Irigoyen, M. M., Nunez-Davis, R., Budacki, J. G., Mohanty, S. H., Leister, K. P., & Bonner, R. L. (2015) · Pediatrics · doi:10.1542/peds.2015-2151 · Cited by 1,053 · see citing works
Digital Advertising to Children
Radesky, J., Chassiakos, Y. (. R., Ameenuddin, N., Navsaria, D., & COUNCIL ON COMMUNICATION AND MEDIA (2020) · Pediatrics · doi:10.1542/peds.2020-1681 · Cited by 158 · see citing works
Self-citationAdvertising in Young Children’s Apps: A Content Analysis
Meyer, M., Adkins, V., Yuan, N., Weeks, H. M., Chang, Y.-J., & Radesky, J. (2019) · Journal of Developmental & Behavioral Pediatrics · doi:10.1097/dbp.0000000000000622 · Cited by 109 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 12
- Vaala S, Ly A, Levine MH. Getting a Read on the App Stores: A Market Scan and Analysis of Children's Literacy Apps. Joan Ganz Cooney Center at Sesame Workshop; 2015.
- Zhao J. Are children well-supported by their parents concerning online privacy risks, and who supports the parents? Published April 28, 2018. Accessed September 1, 2019. https://arxiv.org/pdf/ 1809.10944.pdf
- Yao Y, Lo Re D, Wang Y. Folk models of online behavioral advertising. Paper presented at: 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing; February 1-March 25, 2017; Portland, Oregon. Accessed September 1, 2019. https://www.researchgate.net/ publication/313738097_Folk_Models_of_Online_ Behavioral_Advertising
- Binns R, Lyngs U, Van Kleek M, Zhao J, Libert T, Shadbolt N. Third party tracking in the mobile ecosystem. Accessed September 1, 2019. https:// arxiv.org/pdf/1804.03603.pdf · arXiv
- Reyes I, Wijesekera P, Reardon J, et al "Won't somebody think of the children?" examining COPPA compliance at scale. Proc Priv Enh Technol. 2018; 2018:63-83. doi:10.1515/popets-2018-0021 · DOI
- Lee H, Wong SF, Oh J, Chang Y. Information privacy concerns and demographic characteristics: Data from a Korean media panel survey. Gov Inf Q. 2019;36:294-303. doi:10.1016/j.giq.2019.01.002 · DOI
- Park YJ. Digital literacy and privacy behavior online. Commun Res. 2013;40:215-236. doi:10.1177/ 0093650211418338 · DOI
- Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi:10.1016/j.jbi.2008.08.010 · DOI · Paper page
- Radesky JS, Weeks HM, Ball R, et al. Young children's use of smartphones and tablets. Pediatrics. 2020;146(1):e20193518. doi:10.1542/peds.2019-3518 · DOI · Paper page
- Livingstone S, Third A. Children and Young People's Rights in the Digital Age: An Emerging Agenda. Sage Publications; 2017.
- Kang R, Dabbish L, Fruchter N, Kiesler S. "My data just goes everywhere:" user mental models of the internet and implications for privacy and security. Paper presented at: Eleventh Symposium On Usable Privacy and Security; July 22-24, 2015; Ottawa, Canada. Accessed September 1, 2019. https://www.usenix.org/conference/soups2015/ proceedings/presentation/kang
- Schaub F, Breaux TD, Sadeh N. Crowdsourcing privacy policy analysis: potential, challenges and best practices. it-Inf Technol. 2016;58:229-236. doi: 10.1515/itit-2016-0009 · DOI
Cited by
Sorted by citations
Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., et al. (2021) · Journal of Advanced Nursing · doi:10.1111/jan.14855 · Cited by 453 · see citing works
Interactional theory of childhood problematic media use
Domoff, S. E., Borgen, A. L., & Radesky, J. S. (2020) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.217 · Cited by 220 · see citing works
Self-citationFrom Moral Panic to Systemic Change: Making Child-Centered Design the Default
Radesky, J., & Hiniker, A. (2022) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100351 · Cited by 44 · see citing works
Self-citation
- Authors
- Zhao, F., Egelman, S., Weeks, H. M., Kaciroti, N., Miller, A. L., & Radesky, J. S.
- Affiliations
- University of Michigan (US)
- International Computer Science Institute (US)
- Corresponding author
- Radesky, Jenny S.
- Venue
- JAMA Pediatrics
- Publisher
- American Medical Association (AMA)
- Published
- 2020-12-07
- Volume
- 174
- Issue
- 12
- Pages
- e203345
- ISSN
- 1072-4710 (ISSN-L)
- 2168-6203
- PMC
- PMC7489394
- OpenAlex
- W3084258407
- Cited by
- 41
- References
- 3
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 6 · 2022: 8 · 2021: 7 · 2020: 3
- FWCI
- 8.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Archives of Pediatrics and Adolescent Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1817248)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD094051, R41HD100230, K23HD092626)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Education › Child Development and Digital Technology
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- mobile application, data collection practices, third-party data sharing, persistent identifiers, preschool children, socioeconomic status, sociodemographic factors, Android devices, network traffic monitoring, children's online privacy
- MeSH terms
- Child, Child Welfare, Child, Preschool, Confidentiality, Female, Humans, Play and Playthings, Prospective Studies, Computers, Handheld, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review