{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:12:56Z","timestamp":1788286376103,"version":"build-2803163510"},"reference-count":44,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Pediatr Parent"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Technology use by children has implications for their physical, mental, and social health and development. There are many challenges in measuring modern screen use by children. To better understand the potential impact of technology on children, it is crucial to have robust methods to measure technology use. Visual recording data may be particularly well suited to capture modern digital technology use (eg, rapid transitions, multitasking, and device switching). It would be valuable to directly compare the 2 commonly used methods (wearable camera images and room video frames) to determine what is captured best by each and how they compare when both are coded by human annotation.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to compare wearable camera images and room video frames to understand what can be captured from each source.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In a laboratory study, children (aged 3\u201014 years) performed various tasks while wearing a wearable camera and being video recorded by room video cameras. Comparisons between the coding of the wearable camera images and room video frames were performed on a second-by-second basis. Confusion matrices were generated to identify patterns of differences in classifications between wearable camera images and room video frame coding. Percent agreement between the wearable camera image and room video frame coding was calculated based on the confusion matrix.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Across 44 participants, 182,451 contemporaneous wearable camera images and room video frames were coded and merged. There were generally low levels of agreement between the codes. The highest agreement was found for \u201cdesktop\u201d and \u201chandheld gaming,\u201d and the lowest agreement was found for \u201csmartwatch.\u201d The wearable camera and room video methods varied in sampling rate, camera movement, and field of view, leading to differences in what was captured.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Room video might better capture technology that is within a set space and the context of the technology use, while wearable cameras could better capture active technology use, the content of technology, and different locations. Capturing technology use by children is difficult, with no gold standard and no one ideal method.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/91113","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:14:25Z","timestamp":1788282865000},"page":"e91113-e91113","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Capturing Children\u2019s Technology Use Using Wearable Camera and Fixed Room Video: Experimental Laboratory Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-4429","authenticated-orcid":false,"given":"Amber","family":"Beynon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2193-5019","authenticated-orcid":false,"given":"Charlotte","family":"Lund 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