Background: Television viewing among children is associated with developmental and health outcomes, yet measurement techniques for television viewing are prone to errors, biases, or both.
Objective: This study aims to develop a system to objectively and passively measure children's television viewing time.
Methods: The Family Level Assessment of Screen Use in the Home-Television (FLASH-TV) system includes three sequential algorithms applied to video data collected in front of a television screen: face detection, face verification, and gaze estimation. A total of 21 families of diverse race and ethnicity were enrolled in 1 of 4 design studies to train the algorithms and provide proof of concept testing for the integrated FLASH-TV system. Video data were collected from each family in a laboratory mimicking a living room or in the child's home. Staff coded the video data for the target child as the gold standard. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each algorithm, as compared with the gold standard. Prevalence and biased adjusted κ scores and an intraclass correlation using a generalized linear mixed model compared FLASH-TV's estimation of television viewing duration to the gold standard.
Results: FLASH-TV demonstrated high sensitivity for detecting faces (95.5%-97.9%) and performed well on face verification when the child's gaze was on the television. Each of the metrics for estimating the child's gaze on the screen was moderate to good (range: 55.1% negative predictive value to 91.2% specificity). When combining the 3 sequential steps, FLASH-TV estimation of the child's screen viewing was overall good, with an intraclass correlation for an overall time watching television of 0.725 across conditions.
Conclusions: FLASH-TV offers a critical step forward in improving the assessment of children's television viewing.
Keywords: child; digital media; gaze; machine learning; measurement; mobile phone; screen media; television.
©Anil Kumar Vadathya, Salma Musaad, Alicia Beltran, Oriana Perez, Leo Meister, Tom Baranowski, Sheryl O Hughes, Jason A Mendoza, Ashutosh Sabharwal, Ashok Veeraraghavan, Teresia O'Connor. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 24.03.2022.