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High sleep quality promotes efficient performance in the following day. Sleep quality is influenced by environmental factors, such as temperature, light, sound and smell. Here, we investigated whether differences in the interface pressure distribution on healthy individuals during sleep influenced sleep quality. We defined four types of pressure models by differences in the area distribution and the subjective feelings that occurred when participants slept on the mattresses. One type of model was showed “over-concentrated” distribution of pressure; one was displayed “over-evenly” distributed interface pressure while the other two models were displayed intermediate distribution of pressure. A polysomnography analysis demonstrated an increase in duration and proportion of non-rapid-eye-movement sleep stages 3 and 4, as well as decreased number of micro-arousals, in subjects sleeping on models with pressure intermediately distributed compared to models with over-concentrated or over-even distribution of pressure. Similarly, higher scores of self-reported sleep quality were obtained in subjects sleeping on the two models with intermediate pressure distribution. Thus, pressure distribution, at least to some degree, influences sleep quality and self-reported feelings of sleep-related events, though the underlying mechanisms remain unknown. The regulation of pressure models imposed by external sleep environment may be a new direction for improving sleep quality. Only an appropriate interface pressure distribution is beneficial for improving sleep quality, over-concentrated or -even distribution of pressure do not help for good sleep.

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The harmonic analysis method based on high and low water levels is discussed in this paper. In order to make full use of the information of high and low water observations (the time derivative of water level at the observation time is zero), the weight coefficient, w, is introduced to control the importance of the part related to this information in the error formula. The major diurnal constituents, O1 and K1, and semi-diurnal constituents, N2, M2 and $2 are selected directly from the monthly data analysis, and some other important constituents, P1, vz and K2, are included as the inferred constituents. The obtained harmonic constants of the major constituents are very close to those obtained from the analysis of hourly data, and this shows that high and low water data can be used to extract tidal constants with high accuracy. The analysis result also shows that the inference and the weighting coefficient are important in the high and low water data analysis, and it is suggested that w~l should be taken in monthly high and low water data analysis. This analysis method can be used directly to analyze altimetric data with w = 0.

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