Publication Date
11-30-2024
Journal
Scientific Reports
DOI
10.1038/s41598-024-81136-0
PMID
39616199
PMCID
PMC11608350
PubMedCentral® Posted Date
11-30-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Television, Female, Male, Child, Screen Time, Machine Learning, Child, Preschool, Television, Gaze estimation, Screen media, Machine learning, Face detection, Electrical and electronic engineering, Risk factors
Abstract
TV viewing is associated with health risks, but existing measures of TV viewing are imprecise due to relying on self-report. We developed the Family Level Assessment of Screen use in the Home (FLASH)-TV, a machine learning pipeline with state-of-the-art computer vision methods to measure children's TV viewing. In three studies, lab pilot (n = 10), lab validation (n = 30), and home validation (n = 20), we tested the validity of FLASH-TV 3.0 in task-based protocols which included video observations of children for 60 min. To establish a gold-standard to compare FLASH-TV output, the videos were labeled by trained staff at 5-second epochs for whenever the child watched TV. For the combined sample with valid data (n = 59), FLASH-TV 3.0 provided a mean 85% (SD 8%) accuracy, 80% (SD 17%) sensitivity, 86% (SD 8%) specificity, and 0.71 (SD 0.15) kappa, compared to gold-standard. The mean intra-class correlation (ICC) of child's TV viewing durations of FLASH-TV 3.0 to gold-standard was 0.86. Overall, FLASH-TV 3.0 correlated well with the gold standard across a diverse sample of children, but with higher variability among Black children than others. FLASH-TV provides a tool to estimate children's TV viewing and increase the precision of research on TV viewing's impact on children's health.
Included in
Biochemical Phenomena, Metabolism, and Nutrition Commons, Community Health and Preventive Medicine Commons, Dietetics and Clinical Nutrition Commons, Neurosciences Commons, Nutrition Commons, Pediatrics Commons