Validation of the digital health literacy assessment among the university students in China

PurposeWith the development of the internet, digital health literacy (DHL) has become increasingly important for managing health. Consequently, various digital health literacy scales have been created for different groups. The purpose of this study was to verify the reliability and validity of the simplified Chinese version of the Digital Health Literacy Assessment (DHLA) scale among university students in China.MethodSnowball sampling was used to recruit the participants via an online platform (Wenjuan.com), and finally 304 university students were included in the survey. Demographic information and the status of DHL were collected through the online questionnaire. Cronbach’s alpha and split-half reliability were used to test the internal consistency of the scale, while the structural validity was verified by exploratory factor analysis and confirmatory factor analysis. Additionally, the convergence of the scale was tested by composite reliability (CR) and average variance extracted (AVE).ResultTwo dimensions were generated from 10 entries in the scale, named Self-rated Digital Health Literacy and Trust Degree of Online Health Information, respectively. The Cronbach’s alpha and split-half reliability of the total scale were 0.912 and 0.828, while the Cronbach’s alpha of the two dimensions were 0.913 and 0.830, respectively. The structural validity-related indexes of the scale met the standards (RMSEA = 0.079, GFI = 0.943, AGFI = 0.902, CFI = 0.971). In each dimension, the CR and AVE also reached critical values (CR > 0.7 and AVE > 0.5).ConclusionThe scale had high reliability and validity, indicating the simplified Chinese DHLA scale could be used to evaluate the DHL of university students in China

Wearable Perovskite‐Based Shadow Recognition Sensor for Ambient and Nonobtrusive Human–Computer Interaction

Driven by the Internet of Everything, one of the main goals in human–computer interaction is to achieve intuitive, effortless, and easy‐to‐learn communication. Thus, senseless optoelectronic devices with high response performance under ambient environment have an extensive application space in improving the interfacing between users and computers. Herein, a concept of wearable perovskite‐based shadow recognition sensor is demonstrated for ambient and nonobtrusive human–computer interaction. The multidimensional ordered nucleation and growth of perovskite crystals are promoted by introducing the self‐driving effect of liquid crystal (LC) oligomers. The resulted LC‐doped perovskite film (LC‐PVK) with micrometer‐sized grains can output relatively high photocurrent under indoor ambient light (≈500 lux). The LC‐based device exhibits over a hundred times of on–off ratio and fast response of millisecond level even after storage for more than 1200 h. The device also shows an ultratrace Pb2+ leakage of 1.02 μg L−1 in water, and still retains more than 90% of the photocurrent intensity after thousands of bending strains. Accordingly, a novel human–computer interaction is achieved by identifying external action commands with the recognition of shadows, which can provide a “haptic” perception for robots