Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep

Abstract

There is an identified need for objective, reliable, and scalable methods of measuring and recording sleep. Such methods must be designed for easy integration into people's lives in order to support both sleep therapy and everyday personal informatics. This paper describes the design and evaluation of a mobile phone application to record sleep, the design of which has substantive foundation in clinical sleep research. Two user studies were carried out which demonstrate that the application produces valid measurements of sleep quality and high levels of usability, whilst not seriously disturbing sleep or the sleep environment. These findings suggest that the app is suitable for both everyday sleep monitoring in a personal informatics context, and for integration into sleep interventions

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

“…We also found features in mobile phone usage, wearable sensor and survey data that were significantly related to perceived stress level using correlation analysis. In the current study, we have increased our sampling period to ~30-days per person and our population to 66 participants to collect more intensive multi-modal data including perceived stress, sleep, personality, physiological, behavioral and social interaction data that are important factors in academic performance, sleep, stress, and mental health in addition to what were monitored on the phone in previous studies [4, 5, 8, 9, 10]. …”

EZwakeup

“…Their results revealed that external stimuli would affect sleep quality, even though the stimuli was applied before sleeping. Lawson et al [7] explored if mobile phone application can be used to influence sleep. They produced short 4-second low frequency (95Hz) low volume tone every 15 minutes to observe people's sleep condition changes.…”

Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask

“…For sleep quality evaluation and diagnosis, a subject's polysomnogram (PSG) recordings, which include an electroencephalogram (EEG), an electrooculogram (EOG) and an electromyogram (EMG), are usually measured and scored by an expert, who then classifies each 30-s epoch into one of the following sleep stages: wakefulness (Wake), non-REM sleep (Stages 1-4) and REM sleep (REM). The Wake stage consists of alpha activity (8)(9)(10)(11)(12)(13) or low-voltage mixed frequency activity. Stage S1 consists of related low-voltage mixed activity (3)(4)(5)(6)(7); Stage S2 is of the appearance of sleep spindles and/or K-complexes.…”

“…Several activity-based methods [6], [7], wearable devices [8]- [10] or mobile applications [11], have been proposed to monitor sleep efficiency (wake-sleep states) using accelerometers to detect body movements during sleep. These devices are easy to use but might not support accurate recognition of the five sleep stages or real-time sleep-stage analysis.…”

“…These devices are easy to use but might not support accurate recognition of the five sleep stages or real-time sleep-stage analysis. Most of the existing devices cannot monitor users' sleep stages online but help them to analyze their sleep efficiency only [11]- [13].…”