Using Wearable Cameras to Categorize the Type and Context of Screen-Based Behaviors Among Adolescents: Observational Study

George Thomas; Jason A Bennie; Katrien De Cocker; Fitria Dwi Andriyani; Bridget Booker; Stuart J H Biddle

doi:10.2196/28208

Using Wearable Cameras to Categorize the Type and Context of Screen-Based Behaviors Among Adolescents: Observational Study

JMIR Pediatr Parent. 2022 Mar 21;5(1):e28208. doi: 10.2196/28208.

Authors

George Thomas^#^{1

2}, Jason A Bennie^#², Katrien De Cocker^#^{2

3}, Fitria Dwi Andriyani^#^{2

4}, Bridget Booker^#⁵, Stuart J H Biddle^#²

Affiliations

¹ School of Allied Health, Curtin University, Bentley, Australia.
² Centre for Health Research, University of Southern Queensland, Springfield Central, Australia.
³ Department of Movement and Sports Sciences, University of Gent, Gent, Belgium.
⁴ Department of Sports Education, Yogyakarta State University, Yogyakarta, Indonesia.
⁵ Institute for Positive Psychology and Education, Australian Catholic University, North Sydney, Australia.

^# Contributed equally.

PMID: 35311672
PMCID: PMC8981006
DOI: 10.2196/28208

Abstract

Background: Automated wearable cameras present a new opportunity to accurately assess human behavior. However, this technology is seldom used in the study of adolescent's screen exposure, and the field is reliant on poor-quality self-report data.

Objective: This study aimed to examine adolescents' screen exposure by categorizing the type and context of behaviors using automated wearable cameras.

Methods: Adolescents (mean age 15.4 years, SD 1.6 years; n=10) wore a camera for 3 school evenings and 1 weekend day. The camera captured an image every 10 seconds. Fieldwork was completed between February and March 2020, and data were analyzed in August 2020. Images were date and time stamped, and coded for screen type, content, and context.

Results: Data representing 71,396 images were analyzed. Overall, 74.0% (52,842/71,396) of images contained screens and 16.8% (11,976/71,396) of images contained multiple screens. Most screen exposures involved television sets (25,950/71,396, 36.3%), smartphones (20,851/71,396, 29.2%), and laptop computers (15,309/71,396, 21.4%). The context of screen use differed by device type, although most screen exposures occurred at home (62,455/64,856, 96.3%) and with solitary engagement (54,430/64,856, 83.9%). The immediate after-school period saw high laptop computer use (4785/15,950, 30.0%), while smartphone use (2059/5320, 38.7%) peaked during prebedtime hours. Weekend screen exposure was high, with smartphone use (1070/1927, 55.5%) peaking in the early morning period and fluctuating throughout the day.

Conclusions: There was evidence for high screen use during the after-school and weekend period, mostly through solitary engagement, and within the home environment. The findings may inform the basis of larger studies aimed at examining screen exposure in free-living conditions.

Keywords: adolescent; screen time; smartphone; television.

©George Thomas, Jason A Bennie, Katrien De Cocker, Fitria Dwi Andriyani, Bridget Booker, Stuart J H Biddle. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 21.03.2022.