Evidence map›Paper›PMID 39616199›Full record

ArticleScientific reports2024

Validation studies of the FLASH-TV system to passively measure children's TV viewing.

Anil Kumar Vadathya, Tatyana Garza, Uzair Alam, Alex Ho, Salma M A Musaad, Alicia Beltran, Jennette P Moreno, Tom Baranowski, Nimah Haidar, Sheryl O Hughes and 5 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Anil Kumar VadathyaDepartment of Electrical & Computer Engineering, Rice University, Houston, TX, USA.
Tatyana GarzaUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Uzair AlamUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Alex HoRice University, Houston, TX, USA.
Salma M A MusaadUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Alicia BeltranUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Jennette P MorenoUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Tom BaranowskiUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Nimah HaidarRice University, Houston, TX, USA.
Sheryl O HughesUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Jason A MendozaPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Ashok VeeraraghavanDepartment of Electrical & Computer Engineering, Rice University, Houston, TX, USA.
Joseph YoungDepartment of Electrical & Computer Engineering, Rice University, Houston, TX, USA.
Akane SanoDepartment of Electrical & Computer Engineering, Rice University, Houston, TX, USA.
Teresia M O'ConnorUSDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA. teresiao@bcm.edu.

Funding

Technology and digital media's influence on preschool children's sleep and weight statusP01HD109876 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI Teresia Margareta O'Connor · 2022 to 2026
$7.5M
Development and Validation of an Automated Measurement of Child Screen Media Use: FLASHR01DK113269 · NIDDK · BAYLOR COLLEGE OF MEDICINE · PI O'CONNOR, TERESIA MARGARETA, VEERARAGHAVAN, ASHOK · 2017 to 2021
$2.4M
Eunice Kennedy Shriver National Institute of Child Health and Human Development P01HD109876National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) R01DK113269NICHD NIH HHS P01 HD109876NIDDK NIH HHS R01 DK113269
6 · The paper itself

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.

Indexed as

TelevisionChildChild, PreschoolFemaleHumansMachine LearningMaleScreen TimeFace detectionGaze estimationMachine learningScreen mediaTelevision

Identifiers

PMID39616199
PMCPMC11608350

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.