Factor analyses of a social support scale using two methods.

PURPOSE: Evaluation and comparison of the factor structure of the Medical Outcomes Study Social Support Survey (MOS-SSS) using both confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) with two samples of people living with HIV/AIDS in China. METHODS: Secondary analyses were conducted with data from two comparable samples of 320 people living with HIV/AIDS from the same hospital using the same inclusion criteria. The first sample of 120 was collected in 2006, and the second sample of 200 was collected in 2012. For each sample, CFA was first performed on the original four-factor structure to check model fit, followed by EFA to explore other factor structures and a subsequent CFA for model fit statistics to be compared to the original four-factor CFA. RESULTS: In both samples, CFA on the originally hypothesized four-factor structure yielded an acceptable model fit. The EFA yielded a two-factor solution in both samples, with different items included in each factor for the two samples. Comparison of CFA on the a priori four-factor structure and the new two-factor structure in both samples indicated that both factor structures were of acceptable model fit, with the four-factor model performing slightly better than the two-factor model. CONCLUSION: Factor structure of the MOS-SSS is method-dependent, with CFA supporting a four-factor structure, while EFA yielded a two-factor structure in two separate samples. We need to be careful in selecting the analytic method when applying the MOS-SSS to various samples and choose the factor structure that best fits the theoretical model.

Psychometric testing of the Chinese version of the Medical Outcomes Study Social Support Survey among people living with HIV/AIDS in China.

PURPOSE: The aim of this study was to assess the psychometric properties of the Chinese (Mandarin) version of the Medical Outcomes Study Social Support Survey (MOS-SSS-CM) among people living with HIV/AIDS (PLWHA) in Mainland China. METHODS: A cross-sectional study was conducted with a convenience sample of 200 Chinese PLWHA. They completed the MOS-SSS-CM along with the Chinese version of the Beck Depression Inventory Revised (BDI-II) scale, the Zung Self-Rating Anxiety Scale (SAS), the Perceived Stress Scale (PSS-10), and the World Health Organization Quality of Life Brief (WHOQOL-BREF) scale. RESULTS: Internal consistency (Cronbach's α) was 0.97 for the overall MOS-SSS-CM and 0.82-0.91 for the five subscales originally proposed. However, 11 of the 19 items demonstrated unsatisfactory item discriminant validity. An exploratory factor analysis yielded a two-factor solution with tangible and social-emotional dimensions, which demonstrated satisfactory reliability and better discrimination between different subscales than did the original five-factor model. The concurrent validity of the two-factor scale was further confirmed by its significant negative correlations with the BDI-II (r=-0.41, p<0.01); the SAS (r=-0.27, p<0.01); and the PSS-10 (r=-0.30, p<0.01), and significant positive correlation with the WHOQOL-BREF scale (r=0.61, p<0.01). CONCLUSION: We found a two-factor solution for the MOS-SSS-CM, which demonstrated good reliability and validity when applied to Chinese PLWHA. This was consistent with results from a study of Taiwanese caregivers. Further validation in other populations and disease states is warranted.

Highly sensitive and wide-detection range pressure sensor constructed on a hierarchical-structured conductive fabric as a human-machine interface.

With the booming development of flexible pressure sensors, the need for multifunctional and high-performance pressure sensor has become increasingly important. Although great progress has been made in the novel structure and sensing mechanism of the pressure sensor, the trade-off between the sensitivity and the wide-detection range has prevented its development, further restricting its application in wearable human-machine interfaces (WHMIs). Herein, a novel pressure sensor based on the hierarchical conductive fabric was fabricated and purposed as a WHMI. Poly(3,4-ethylenedioxythiophene) nanowires (PEDOT NWs) and cellulose nanofibers (CNF) were stacked on a conductive poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) fabric to form a special spatial multi-level hierarchical structure inside the fabric, which is a breakthrough for the improvement of the sensor's performance and makes the fabrication process of in situ polymerization suitable for large-scale production. The multi-level hierarchical structures endowed the pressure sensor with characteristics of high sensitivity (15.78 kPa-1), a wide-detection range from 30 Pa to 700 kPa, and outstanding stability toward compression and bending deformation. Benefiting from its excellent performance, a human-machine interface based on arrayed pressure sensors and signal processing system can control the illumination of the LED array and effectively capture finger motion to control the eight-direction movement of an unmanned aerial vehicle (UAV). This improved performance of the pressure sensor based on the hierarchical conductive fabric made it a widespread application in intelligent fabric, electronic skin, human-machine interfaces, and robotics.