Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. We find that both sociodemographic as well as smartphone-related characteristics are associated with how people use their smartphones, and that this affects the suitability of smartphone data for measuring everyday activities. Against this background, we surveyed two independent, large-scale samples of German smartphone owners (n1 = 3956 n2 = 2525) on how they use their smartphones, with a focus on three everyday activities: mobility, physical activity, and sleep. Not having the smartphone in close proximity throughout the day, turning the device off, or sharing the device with others can constitute barriers that interfere with accurately measuring everyday activity with data from the phone's native sensors. The success of inference from raw smartphone sensor data to activity outcomes depends, among other factors, on how smartphone owners use their device. For example, that a person is sleeping might be inferred from the fact that their phone is idle and that there is no sound and light around the phone. Researchers take advantage of this phenomenon by using data from smartphone sensors to infer everyday activities, such as mobility, physical activity, and sleep. Smartphones have become central to our daily lives and are often present in the same contexts as their users. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness. The work proved to be potentially helpful in the early investigation of sleep in the home environment. QS proved to be positively correlated (0.72☐.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85☐.007) to the Apnea-Hypopnea Index (AHI). In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. All underwent full PSG and PBS recordings. The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases.
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