Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems 2017
DOI: 10.1145/3025453.3025557
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Making Sense of Sleep Sensors

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Cited by 80 publications
(39 citation statements)
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“…Thus, tools that support such monitoring may support clinicians in being more effective in their roles. According to existing literature, students are interested in self-experimentation and learn self-management skills from personal experience [19,20,21]. Therefore, for students who may not be fully aware of the associations between their behaviors and depression, it is critical to provide integrated data visualizations to facilitate sense making of behavioral targets.…”
Section: Discussionmentioning
confidence: 99%
“…Thus, tools that support such monitoring may support clinicians in being more effective in their roles. According to existing literature, students are interested in self-experimentation and learn self-management skills from personal experience [19,20,21]. Therefore, for students who may not be fully aware of the associations between their behaviors and depression, it is critical to provide integrated data visualizations to facilitate sense making of behavioral targets.…”
Section: Discussionmentioning
confidence: 99%
“…Congruent with the rise of consumer health technologies, HCI research has increasingly examined the use of personal informatics tools [2] involving physical activity [27,30], food intake [15,16], sleeping behaviour [52], productivity [13], mental wellness [34], menstrual cycles [23], disease progression [3] and care-giving [63]. Rooksby et al [15] characterise these self-tracking practices as 'lived' -enmeshed in everyday life -and identify overlapping selftracking styles, such as documentary tracking and diagnostic tracking.…”
Section: Self-tracking With Personal Informatics Systemsmentioning
confidence: 99%
“…This information is produced based on data from various sensors: devices worn on the wrist (like Fitbit) often contain accelerometers and optical heart rate sensors, whereas devices worn as a sleep mask (like Neuroon) also contain electroencephalography to record electrical activity of the brain. In principle, electroencephalography can provide more accurate insight into sleep stages, but the downside is that sensors worn on the head can be more obtrusive than wrist-worn sensors and hence interfere with sleep [77]. In other words, in choosing a consumer sleep-tracking device, users are faced with a trade-off between data quality and other factors like physical appearance and price.…”
Section: Introductionmentioning
confidence: 99%
“…For example, simple usability issues like battery limits and lack of comfort as well as irregular sleep patterns make it difficult to collect useful information [61]. Lack of contextual information and limited knowledge about sleep and sleep stages can make it difficult to interpret sleep information and to take actions from it [61,77]. However, these HCI studies are not concerned with the accuracy of sleep information, or the credibility that people place in such information.…”
Section: Introductionmentioning
confidence: 99%