Wearable sensor and algorithm for automated measurement of screen time

Fletcher,; Chamberlain,; Richman,; Oreskovic,; Taveras,

doi:10.1109/wh.2016.7764564

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…Table 3 reports the methodologic quality of the included studies. Four studies were rated as Excellent [23,46,47,49], four as Good [38,39,43,54], twelve as Fair [19, 34, 36, 37, 40, 48, 50- [20,33,35,41,42,44,45,56,58]. The poor ratings were due to a small sample size (<30) [35,41,42,44,45,56,58] and/or not reporting correlations or AUC in their results [20,33,56].…”

Section: Methodological Qualitymentioning

confidence: 99%

“…Two studies [56,57] measured screen media use among more than one age group: Lee [57] measured mobile phone use among adolescents and young adults, and Bechtel [56] measured TV viewing time among adults and children. One study [58] did not report the ages of their study participants.…”

Section: Diversity Of Participantsmentioning

confidence: 99%

“…Three studies [34,37,55], using self-report approaches, looked at screen media use on specific social media sites like Facebook and Twitter on a computer screen. Four studies [35,36,53,58] measured more than one screen including TVs, computers, laptops, and mobile phones and tablets, with two testing self-report [35,53] and two testing technology-based assessments [35,58].…”

Section: Variety Of Screens Measuredmentioning

confidence: 99%

“…Tools for measuring multiple screen media platforms. Fletcher et al [58] and Zhang & Rehg [35] used novel devices to measure screen media use on a variety of screens. The former tested an optical color sensor with accelerometer worn on the wrist.…”

Section: Plos Onementioning

confidence: 99%

“…The sensor was tested while the participants performed various activities in front of a TV, computer or laptop screen, and under varied lighting conditions. Fletcher et al's [58] preliminary data suggest their machine learning algorithms can differentiate the type of screen to which the participant was exposed (TV, computer or laptop). They reported scores of AUC of 0.90 for detecting a TV screen, 0.89 for detecting a computer screen, 0.83 for detecting both screens are present near the device.…”

Section: Plos Onementioning

confidence: 99%

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Validated assessment tools for screen media use: A systematic review

et al. 2023

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Objective Accurate measurement of adult and child screen media use are needed to robustly assess its impact on health outcomes. Our objective was to systematically review screen media use measurement tools that have been validated against an objective “gold standard” tool. Methods The search strategy was initially conducted in Medline Ovid and translated to Embase, Web of Science, PsychInfo and Cochrane. A modified natural language search was conducted in Google Scholar and IEEE. The initial search was conducted in March 2021, and an updated search was conducted in June 2022. Additional studies were included from the references. Studies had to describe the validation of a tool to measure screen media use on participants of any age against a ‘gold standard’ or comparable objective measure. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) was used to assess the criterion validity. Four authors reviewed the titles in two rounds and extracted data. Results Twenty-nine articles were included in the review. Studies measured TV, computer, mobile device and social media site screen media use through: self or parent report, direct or video observation, computer and mobile device use tracking programs, and through other novel devices such as wearable devices and cameras. Correlations of self or parent report of screen media with the gold standard were lower than correlations of technology-based measures, and video observation with the gold standard. The COSMIN criterion validity ratings ranged from poor to excellent; most of the studies received a global score of fair or poor. Conclusions Technology based validated tools that more directly measure screen use are emerging that have been validated against a gold standard for measuring screen use. However, practical, objective measures of diverse types of screen media use that have been tested on diverse populations are needed to better understand the impact of screen media use on the development and physical and mental health of children and adults.

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Section: Methodological Qualitymentioning

confidence: 99%

Section: Diversity Of Participantsmentioning

confidence: 99%

Section: Variety Of Screens Measuredmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Validated assessment tools for screen media use: A systematic review

et al. 2023

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Application of wearable devices for monitoring cardiometabolic dysfunction under the exposome paradigm

Zhang

Zheng

et al. 2023

Chronic Diseases and Translational Medicine

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Environmental factors, including chemical/physical pollutants, as well as lifestyle and psychological factors, contribute greatly to the pathways leading to cardiometabolic diseases with a heavy disease burden and economic loss. The concept of exposomes provides a novel paradigm for combining all exposure characteristics to evaluate disease risk. A solution‐like exposome requires technological support to provide continuous data to monitor vital signs and detect abnormal fluctuations. Wearable devices allow people to conveniently monitor signals during their daily routines. These new technologies empower users to more actively prevent and manage cardiometabolic disease by reviewing risk factors of the disease, especially lifestyle factors, such as sleeping time, screen time, and mental health condition. Devices with multiple sensors can monitor electrocardiography data, oxygen saturation, intraocular pressure, respiratory rate, and heart rate to enhance the exposome study and provide precise suggestions for disease prevention and management.

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