Evaluation of the Square Eyes Model as a Screening Tool for Identifying Digital Technologies in Wearable Camera Images Among Children: Laboratory Study - Report - MDSpire
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Assessment of the Square Eyes Model as a Tool for Screening Digital Technologies in Wearable Camera Images of Children: A Laboratory Investigation

  • By

  • Charlotte Lund Rasmussen

  • Taren Sanders

  • Erin Kaye Howie

  • Amber Beynon

  • Danica Hendry

  • Juliana Zabatiero

  • Amity Campbell

  • Leon Straker

  • September 14, 2026

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Clinical Report: Assessment of the Square Eyes Model for Screening Digital Tech

Background

The increasing exposure of children to digital technologies raises concerns regarding their health and well-being. Traditional methods of assessing technology use, such as self-reports, are often compromised by biases. Objective measurement techniques, like wearable cameras, may offer a more accurate understanding of children's interactions with digital media.

Data Highlights

No numerical or trial data was provided in the source material.

Key Findings

  • The Square Eyes Model aims to objectively assess children's technology use through wearable cameras.
  • Traditional self-reported methods may introduce recall and social desirability biases.
  • Wearable cameras can accurately record the timing and nature of children's technology interactions.
  • Understanding the context of technology use is essential for evaluating its impact on children's health.
  • There is a complex relationship between screen time and various health outcomes in children.

Clinical Implications

Healthcare professionals should consider the limitations of self-reported measures when assessing children's technology use. The adoption of objective measurement tools like wearable cameras is suggested to improve evaluations in clinical settings.

Conclusion

The Square Eyes Model represents an advancement in the objective assessment of children's digital technology use.

Related Resources & Content

  1. Thomas G, Bennie JA, De Cocker K, et al., Child Ind Res, 2020 -- A descriptive epidemiology of screen-based devices by children and adolescents: a scoping review of 130 surveillance studies since 2000
  2. Mann SC, Lenhart A, Robb MB, Common Sense Media, 2025 -- The common sense census: media use by kids age zero to eight
  3. Li C, Cheng G, Sha T, et al., Int J Environ Res Public Health, 2020 -- The relationships between screen use and health indicators among infants, toddlers, and preschoolers: a meta-analysis and systematic review
  4. Sanders T, Noetel M, Parker P, et al., Nat Hum Behav, 2024 -- An umbrella review of the benefits and risks associated with youths’ interactions with electronic screens
  5. MDSpire News — AI Model Helps ID Eye Diseases from Smartphone Images
  6. Frontiers in Medicine — Artificial intelligence-based quantitative analysis of interocular retinal vascular differences in school-age children with mild to moderate anisometropia
  7. JAMA Ophthalmology — Diagnostic Accuracy of a Retinal Birefringence Scanning Device Compared With a Traditional Autorefraction
  8. the ophthalmologist — Detecting Anemia through the Eye
  9. Digital Ecosystems, Children, and Adolescents: Policy Statement | Pediatrics | American Academy of Pediatrics
  10. WHO Guidelines on Physical Activity and Sedentary Behaviour - NCBI Bookshelf
  11. Screen time and online harms: resources for members | RCPCH
  12. Executive Summary - Guidelines on Physical Activity, Sedentary Behaviour and Sleep for Children under 5 Years of Age - NCBI Bookshelf
  13. Nonlinear dose-response relationships between screen time–based sedentary behavior and depression in adolescents: a systematic review and multilevel meta-analysis - ScienceDirect
  14. Association of Screen Time With Internalizing and Externalizing Behavior Problems in Children 12 Years or Younger: A Systematic Review and Meta-analysis | Media and Youth | JAMA Psychiatry | JAMA Network
  15. JMIR Pediatrics and Parenting - Comparison of Capturing Children’s Technology Use Using Wearable Camera and Fixed Room Video: Experimental Laboratory Study
  16. The influence of different processing rules on wearable camera data estimates of habitual screen time in children - PubMed
  17. Screen Detection from Egocentric Image Streams Leveraging Multi-View Vision Language Model - PMC
  18. Evaluation of the Square Eyes model as a screening tool for identifying digital technologies in wearable camera images among children (Preprint) | Request PDF

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