The findings suggest that crowdsourcing holds potential to provide basic feedback on overall diet quality to users utilizing a low burden approach.

Poor sleep quality is associated with chronic diseases, weight increase and cognitive dysfunction. Home monitoring solutions offer the possibility of offering tailored sleep coaching interventions. There are several new commercially available devices for tracking sleep, and although they have been tested in sleep laboratories, little is known about the errors associated with the use in the home. To address this issue we performed a study in which we compared the sleep monitoring data from two commercially available systems: Fitbit One and Beddit Pro. We studied 23 subjects using both systems over a week each and analyzed the degree of agreement for different aspects of sleep. The results suggest the need for individual-tailoring of the estimation process. Not only do these models address improved accuracy of sleep quality estimates, but they also provide a framework for the representation and harmonization for monitoring data across studies.

The aim of this study is to explore the capability of an Emfit (electromechanical film transducer) mattress to detect snoring (SN) by analyzing the spectral differences between normal breathing (NB) and SN. Episodes of representative NB and SN of a maximum of 10 min were visually selected for analysis from 33 subjects. To define the bands of interest, we studied the statistical differences in the power spectral density (PSD) between both breathing types. Three bands were selected for further analysis: 6-16 Hz (BW1), 16-30 Hz (BW2) and 60-100 Hz (BW3). We characterized the differences between NB and SN periods in these bands using a set of spectral features estimated from the PSD. We found that 15 out of the 29 features reached statistical significance with the Mann-Whitney U-test. Diagnostic properties for each feature were assessed using receiver operating characteristic analysis. According to our results, the highest diagnostic performance was achieved using the power ratio between BW2 and BW3 (0.85 area under the receiver operating curve, 80% sensitivity, 80% specificity and 80% accuracy). We found that there are significant differences in the defined bands between the NB and SN periods. A peak was found in BW3 for SN epochs, which was best detected using power ratios. Our work suggests that it is possible to detect snoring with an Emfit mattress. The mattress-type movement sensors are inexpensive and unobtrusive, and thus provide an interesting tool for sleep research.

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