This study aimed to evaluate the relative validity and reproducibility of a semiquantitative food frequency questionnaire (SFFQ) in adult populations in China. Among the 49 recruited healthy participants (age range: 20–60 years), the relative validity of a 79-item SFFQ was assessed in two ways: (1) by comparing its dietary intake estimates with those from the average measurements of three inconsecutive 24 h dietary records (24-HDRs); and (2) by comparing its estimates of dietary fatty acids with the measured plasma levels of fatty acids. The reproducibility of the SFFQ was evaluated by a comparison of two SFFQ measurements from the same participants collected one year apart. In the relative validity study, the average Spearman correlation coefficient (r) was 0.27 among 18 prespecified food group intakes estimated from the SFFQ and the 24-HDRs; nevertheless, that of five food group intakes (e.g., red meat and seafood) was higher (all rs > 0.40, p < 0.05). In addition, a moderate correlation between the SFFQ estimate of polyunsaturated fatty acid intakes (energy-adjusted percentage of total fatty acids) and its plasma level was observed (r = 0.42, p < 0.05). Regarding the one-year reproducibility of the SFFQ-assessed intakes, the average rank intraclass correlation coefficient (ICC) was 0.35 for the 18 food group estimates. In particular, moderately reproducible estimates of seven food group intakes (e.g., refined grains and red meat, all ICCs ≥ 0.40, p < 0.05) were observed. In conclusion, the SFFQ provides valid and reproducible estimates of dietary intakes for various food groups in general and performs well as a potential tool for estimating habitual dietary intakes of some unsaturated fatty acids.

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A standard operating procedure for studying the sleep phenotypes in a large population cohort is proposed. It is intended for academic researchers in investigating the sleep phenotypes in conjunction with the clinical sleep disorders assessment guidelines. The protocol refers to the definitive American Academy of Sleep Medicine (AASM) manual for setting polysomnography (PSG) technical specifications, scoring of sleep and associated events, etc. On this basis, it not only provides a standardized procedure of sleep interview, sleep-relevant questionnaires, and laboratory-based PSG test, but also offers a comprehensive process of sleep data analysis, phenotype extraction, and data storage. Both the objective sleep data recorded by PSG test and subjective sleep information obtained by the sleep interview and sleep questionnaires are involved in the data acquisition procedure. Subsequently, sleep phenotypes can be characterized by observable/inconspicuous physiological patterns during sleep from PSG test or can be marked by sleeping habits like sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, daytime dysfunction, etc., from sleep interview or questionnaires derived. In addition, solutions to the problems that may be encountered during the protocol are summarized and addressed. With the protocol, it can significantly improve scientific research efficiency and reduce unnecessary workload in large population cohort studies. Moreover, it is also expected to provide a valuable reference for researchers to conduct systematic sleep research.

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Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitive measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.