ArticleJournal of activity, sedentary and sleep behaviors2026
The influence of different processing rules on wearable camera data estimates of habitual screen time in children.
Article in Journal of activity, sedentary and sleep behaviors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough interest in objective screen time measurement is growing, questions remain regarding data processing. The aim of this study was to investigate how different image capture intervals and processing assumptions influence screen time estimates from wearable camera images.
methodsScreen time was measured using chest-worn Brinno TLC130 cameras which took static images every two seconds over four days in children participating in a crossover trial manipulating sleep. Images were coded for screen usage and only children with data from the same time block (before school, after school, weekends) during both intervention conditions were included. Analyses compared estimates of screen time using different intervals of image capture (2, 4, 6, 8, 10, 20, 30, 60 s) and processing rules (images with screens only, Rules 1 and 2 which allowed for blocked [device being used not visible in photos] images as long as the surrounding images were coded as screen time).
results51 children (51% female, 14% indigenous Māori) had sufficient data (187 observations, 1.8 million images). Intervals of up to 60 s between images did not meaningfully influence total screen time estimates compared to 2 s, at the group level. At the individual level, a 10-s interval provided the optimal balance between reducing the number of images to code and accurate screen time estimates. Allowing blocked images between successive screen images to be coded as screen time increased screen time by a median of 8.8 (25th,75th percentiles: 4.4,14.9, Rule 1) to 59.8 (28.6,87.4, Rule 2) minutes.
conclusionResearchers can confidently use up to 60 s intervals between images to measure total screen use in children at the group level, but shorter intervals are required for individual level data. Processing rules which allow blocked images to be coded as screens may influence average screen time estimates by up to 32%.
trial registrationAustralian New Zealand Clinical Trials Registry ANZCTR ACTRN12618001671257, 10th Oct 2018.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.