Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City.

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Understanding the travel challenges and gaps for older adults during the COVID-19 outbreak: Insights from the New York City area.

The COVID-19 pandemic has greatly impacted lifestyles and travel patterns, revealing existing societal and transportation gaps and introducing new challenges. In the context of an aging population, this study investigated how the travel behaviors of older adults (aged 60+) in New York City were affected by COVID-19, using an online survey and analyzing younger adult (aged 18-59) data for comparative analysis. The purpose of the study is to understand the pandemic's effects on older adults' travel purpose and frequency, challenges faced during essential trips, and to identify potential policies to enhance their mobility during future crises. Descriptive analysis and Wilcoxon signed-rank tests were used to summarize the changes in employment status, trip purposes, transportation mode usage, and attitude regarding transportation systems before and during the outbreak and after the travel restrictions were lifted. A Natural Language Processing model, Gibbs Sampling Dirichlet Multinomial Mixture, was adopted to open-ended questions due to its advantage in extracting information from short text. The findings show differences between older and younger adults in telework and increased essential-purpose trips (e.g., medical visits) for older adults. The pandemic increased older adults' concern about health, safety, comfort, prices when choosing travel mode, leading to reduced transit use and walking, increased driving, and limited bike use. To reduce travel burdens and maintain older adults' employment, targeted programs improving digital skills (telework, telehealth, telemedicine) are recommended. Additionally, safe, affordable, and accessible transportation alternatives are necessary to ensure mobility and essential trips for older adults, along with facilitation of walkable communities.

Relationships of adiponectin to regional adiposity, insulin sensitivity, serum lipids, and inflammatory markers in sedentary and endurance-trained Japanese young women.

Introduction: This study aims to compare the differences in circulating adiponectin levels and their relationships to regional adiposity, insulin resistance, serum lipid, and inflammatory factors in young, healthy Japanese women with different physical activity statuses. Methods: Adipokines (adiponectin and leptin), full serum lipid, and inflammatory factors [white blood cell counts, C-reactive protein, tumor necrosis factor-α, tissue plasminogen activator inhibitor-1 (PAI-1)] were measured in 101 sedentary and 100 endurance-trained healthy Japanese women (aged 18-23 years). Insulin sensitivity was obtained through a quantitative insulin-sensitivity check index (QUICKI). Regional adiposity [trunk fat mass (TFM), lower-body fat mass (LFM), and arm fat mass (AFM)] was evaluated using the dual-energy X-ray absorptiometry method. Results: No significant difference was observed between the sedentary and trained women in terms of adiponectin levels. The LFM-to-TFM ratio and the high-density lipoprotein cholesterol (HDL-C) were the strong positive determinants for adiponectin in both groups. Triglyceride in the sedentary women was closely and negatively associated with adiponectin, as well as PAI-1 in the trained women. The QUICKI level was higher in the trained than sedentary women. However, no significant correlation between adiponectin and insulin sensitivity was detected in both groups. Furthermore, LFM was associated with a favorable lipid profile against cardiovascular diseases (CVDs) in the whole study cohort, but this association became insignificant when adiponectin was taken into account. Conclusions: These findings suggest that adiponectin is primarily associated with regional adiposity and HDL-C regardless of insulin sensitivity and physical activity status in young, healthy women. The associations among adiponectin, lipid, and inflammatory factors are likely different in women with different physical activity statuses. The correlation of LFM and a favorable lipid profile against CVD and adiponectin is likely involved in this association.

Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos.

Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h' high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately.

Tumor Organoid Model and Its Pharmacological Applications in Tumorigenesis Prevention.

Cancer is a leading cause of death and a severe threat to global public health. Organoid, as a novel 3D in vitro model, has been applied in various tumor related studies due to its apparent advantages. The organoid is mainly constructed by Matrigel-depended 3D culture system, Air-Liquid Interface (ALI) culture, and Microfluidic culture or Organ-on-chips platform. For the application in carcinogenesis studies, the organoid model may favor depicting initiative hallmarks and identifying potential intervening targets, investigating driver genes of carcinogenesis, and identifying known or unknown risk or protective factors. In this review, we discussed different organoid construction methods and their properties. We also noted that tumor organoids can portray initiative hallmarks and identify possible intervening targets, as well as explore carcinogenesis driver genes and uncover known or unknown risks or protective factors. Organoid systems have been used to identify tumor-preventive drugs such as oligomeric proanthocyanidins, Vitamin D, n-3 PUFAs, and pomegranate. The current evidence underscores the organoid model's potential importance in developing innovative tumorprevention techniques.

Exenatide improves hepatocyte insulin resistance induced by different regional adipose tissue.

Obesity is resulted from energy surplus and is characterized by abnormal adipose tissue accumulation and/or distribution. Adipokines secreted by different regional adipose tissue can induce changes in key proteins of the insulin signaling pathway in hepatocytes and result in impaired hepatic glucose metabolism. This study aimed to investigate whether exenatide affects key proteins of IRS2/PI3K/Akt2 signaling pathway in hepatocytes altered by the different regional fat depots. Six non-obese patients without endocrine diseases were selected as the research subjects. Their subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT)were co-cultured with HepG2 cells in the transwell chamber. In the presence or absence of exenatide, adipokines content in the supernatant of each experimental group was detected by ELISA. In addition, HepG2 cells in each co-culture group with and without insulin were collected, and the expression of key proteins IRS2, p-IRS2(S731), PI3K-p85, Akt2, and p-Akt2(S473) was detected by western blotting (WB). The results showed that the adipokines IL-8, MCP-1, VEGF, and sTNFR2 in the supernatant of HepG2 cells induced by different regional adipose tissue were significantly higher than those in the HepG2 group, and VAT released more adipokines than SAT. Furthermore, these adipokines were significantly inhibited by exenatide. Importantly, the different regional fat depot affects the IRS2/PI3K/Akt2 insulin signaling pathway of hepatocytes. Exenatide can up-regulate the expression of hepatocyte proteins IRS2, PI3K-p85, p-Akt2(S731) inhibited by adipose tissue, and down-regulate the expression of hepatocyte proteins p-IRS2(S731) promoted by adipose tissue. The effect of VAT on the expression of these key proteins in hepatocytes is more significant than that of SAT. But there was no statistical difference in the expression of Akt2 protein among each experimental group, suggesting that exenatide has no influence on the expression of Akt2 protein in hepatocytes. In conclusion, exenatide may improve hepatic insulin resistance (IR) by inhibiting adipokines and regulating the expression of key proteins in the IRS2/PI3K/Akt2 pathway.

A functional approach for characterizing safety risk of signalized intersections at the movement level: An exploratory analysis.

Safety evaluation of signalized intersections is often conducted by developing statistical and data-driven methods based on data aggregated at certain temporal and spatial levels (e.g., yearly, hourly, or per signal cycle; intersection or approach leg). However, such aggregations are subject to a major simplification that masks the underlying spatio-temporal safety risk patterns within the data aggregation levels. Consequently, high-resolution analysis such as safety risk within signal cycles and at traffic movement level cannot be performed. This study contributes to the literature by proposing a new functional data analysis (FDA) approach for a novel characterization of safety risk patterns of signalized intersections. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety risk are proposed to model the time series of safety risk within signal cycles at the traffic movement level. Functional analysis of variance method (FANOVA) that can compare the group level differences of functional curves is used to test differences of safety risk functions among different traffic movements. A typical signalized intersection with representative signal types and channelizations is selected as the study location and approximately 1-hour traffic video data recorded by an unmanned aerial vehicle are used to extract traffic conflicts. New movement-level safety risk patterns are characterized based on the safety risk functions that can reveal the temporal distribution of risk within signal cycles. Most of the tested traffic movements have significantly distinct functional risk patterns according to the FANOVA results while risk patterns for most of the traffic movements cannot be differentiated based on the data aggregated at the cycle and approach levels. The proposed functional approach has the potential to be used for facilitating proactive safety management, calibrating microsimulation models for safety evaluation, and optimizing signal timing while considering traffic safety at more disaggregated levels.

Unusual Role of Point Defects in Perovskite Nickelate Electrocatalysts.

Low-cost transition-metal oxide is regarded as a promising electrocatalyst family for an oxygen evolution reaction (OER). The classic design principle for an oxide electrocatalyst believes that point defect engineering, such as oxygen vacancies (VO..) or heteroatom doping, offers the opportunities to manipulate the electronic structure of material toward optimal OER activity. Oppositely, in this work, we discover a counterintuitive phenomenon that both VO.. and an aliovalent dopant (i.e., proton (H+)) in perovskite nickelate (i.e., NdNiO3 (NNO)) have a considerably detrimental effect on intrinsic OER performance. Detailed characterizations unveil that the introduction of these point defects leads to a decrease in the oxidative state of Ni and weakens Ni-O orbital hybridization, which triggers the local electron-electron correlation and a more insulating state. Evidenced by first-principles calculation using the density functional theory (DFT) method, the OER on nickelate electrocatalysts follows the lattice oxygen mechanism (LOM). The incorporation of point defect increases the energy barrier of transformation from OO*(VO) to OH*(VO) intermediates, which is regarded as the rate-determining step (RDS). This work offers a new and significant perspective of the role that lattice defects play in the OER process.

MiR-26a regulated adipogenic differentiation of ADSCs induced by insulin through CDK5/FOXC2 pathway.

OBJECTIVE: Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes, since insulin can induce adipogenic differentiation of human adipose-derived stem cells (ADSCs). MiR-26a was reported to be highly expressed in ADSCs under induction and Forkhead box C2 (FOXC2), as a key substrate of cyclin-dependent kinase 5 (CDK5) could inhibit white adipocyte differentiation, which was mediated by miR-26a. However, the relationship between miR-26a and CDK5/FOXC2 during ADSCs differentiation remains unknown. We want to verify the regulated mechanism of miR-26a/CDK5/FOXC2 axis participating in the adipogenic differentiation of ADSCS. METHODS: ADSCs were isolated and verified by flow cytometry. Oil Red O staining was performed to assess the capacity for adipogenic differentiation of ADSCs. The proliferation ability of ADSCs was verified by MTT assay. The expression of miR-26a, peroxisome proliferator-activated receptors γ (PPARγ), CDK5, and FOXC2 were tested by qRT-PCR and Western blot, and the relationship between miR-26a and CDK5 was verified by dual-luciferase reporter gene assay. RESULTS: MiR-26a and PPARγ were upregulated and CDK5 and FOXC2 were downregulated during adipogenic differentiation of ADSCs. Knockdown of miR-26a or overexpression of CDK5 could inhibit adipogenic differentiation of ADSCs induced by insulin. MiR-26a could directly target CDK5 and the effect of miR-26a inhibitor on adipogenic differentiation of ADSCs could be blocked by si-CDK5. CONCLUSION: We demonstrated that miR-26a regulated insulin-induced adipogenic differentiation of ADSCs by regulating CDK5/FOXC2 pathway, which could provide the key to a comprehensive mechanistic understanding of obesity and type 2 diabetes.

Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic.

Introduction: The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between "social distancing," a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. Methods: There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. Results: The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.

Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle.

The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle-two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.

Integration of internet health and "Healthy China" strategy / 上海预防医学

In the fifth scientific and technological revolution， information technology is the first productivity， which has a great impact on the supply and demand of medical services. Generally， internet medicine is equivalent to the combination between health industry and information technology. "Healthy China" strategy is China's "priority development strategy"， which adheres to the principle of health equity， emphasizes the integration of health into all policies， and takes co-construction and sharing as the basic path. With implementing internet medicine， "Healthy China" strategy promotes the mobility of medical service with three tools： interconnection， data and artificial intelligence. This enhances the operation efficiency of overall medical and health system， and optimizes the allocation of medical resources. The future development of internet medicine follows the double helix mode driven by technology and policy， and the policy determines the development boundary of the industry. On the basis to ensure medical safety， we should explore the possibility of internet diagnosis and treatment further， and pay attention to the fairness of resource allocation while improving efficiency， so as to realize the co-construction and sharing of health services.

Current situation and prospect of internet medicine development / 上海预防医学

Internet medicine is not a concept of jurisprudence. China's current medical law system mainly focuses on main qualifications of the participants who conduct diagnosis and treatment. This paper focuses on several business models of the core element （diagnosis and treatment） in internet medicine， namely： online health consultation provided by internet medical platform， online diagnosis and treatment by internet hospital， and certain artificial intelligence（AI） medicine products that are specialized for diagnosis and treatment. This paper summarizes the development of these three business models， and analyzes the main problems in their development， including the legitimacy identification of online consultation， the dilemma of the development of internet hospitals， the impact of insufficient sample data on the development of medical AI， and the reverse restriction of regulatory difficulties to the development of internet medicine. The next step of developing internet medicine is to broaden the scope of internet diagnosis and treatment on the premise of medical safety， which will be based on the improvement of current regulatory system. It is urgent to build early risk warning system and supervision/ management mechanism for internet medicine.

Risk analysis of internet medicine regulation / 上海预防医学

The penetration and integration of internet with medical industry is apparently falling behind its interaction with other industries in terms of speed and scale. The main reason is due to the high risk and difficult supervision of internet medicine. Based on the regulatory context， this paper analyzes the endogenous and exogenous risk of internet medicine. The inherent characteristics of the internet （such as openness， diversity， multi subjectivity， virtuality， transparency， and high aggregation） lead to endogenous risks， including jurisdiction disputes， uncertainty of rights and obligations， security of personal information and medical information， monopoly. The lag of specific law in internet medicine， the lack of extensibility and synergy of traditional hierarchical regulatory system， the uncertainty of internet hospital regulatory policy details， the lack of technical specifications/operating procedures and other external system coverage in internet diagnosis and treatment lead to regulatory blind areas and exogenous risks. Based on the risk analysis， this paper discusses further construction of the mechanisms of risk assessment and regulation for internet medicine.

Path analysis of internet medicine supervision and management / 上海预防医学

As a new form of medical industry， internet medicine has not only the endogenous risk of internet industry， but also the blind area and exogenous risk because of lagging regulation. With the continuous progress of internet information technology， online diagnosis and treatment will gradually integrate and reconstruct the traditional medical service. In the history of medical development， the emergence of new technologies generally has the characteristics of risk. Therefore， the supervision of internet medicine should adhere to the rule of inclusiveness and prudence， and take into account the cultivation of the market and standardized development. It should also build a regulatory community， coordinate governance from the four dimensions of legitimacy， compliance， rationality and superiority. Taking the law as the bottom line， we should make full use of early online warning monitoring， off-site supervision， online supervision， reputation mechanism and other measures to promote the innovative development of internet medicine under the framework of legal compliance.

Exosomal MIF Derived From Nasopharyngeal Carcinoma Promotes Metastasis by Repressing Ferroptosis of Macrophages.

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the head and neck cancer (HNC). Metastasis is the main cause of treatment failure. However, the molecular mechanism for NPC metastasis is still unclear. As one of the most common host immune cells in the tumor microenvironment, macrophages have been proven to regulate metastasis. Besides, exosomes are the important bridge connecting various cells in TME. Currently, the role of NPC-exos on macrophages and their impact on metastasis remains to be unexplored. In this study, we found that MIF was highly expressed in NPC cells, and the exosomes secreted by NPC cells could be taken up by macrophages, thereby, inhibiting the ferroptosis of macrophages and then promoting the metastasis of NPC. Targeting MIF may be a potential treatment to reduce the rate of metastasis.

Effect of Insulin-Regulated FOXC2 Expression in Adipocyte Differentiation and Insulin Resistance.

OBJECTIVE: 1) To investigate the effect of FOXC2 on the differentiation of adipose-derived mesenchymal stem cells. 2) To analyze the mechanism between FOXC2 expression regulation in adipose differentiation and insulin resistance (IR). METHODS: We first amplified the FOXC2 promoter region-512 and cloned it into the luciferase expression vector. The reporter gene system was transfected into the adipose tissue-derived mesenchymal stem cell to study insulin-mediated FOXC2 expression. We also manipulated FOXC2 protein expression by either siRNA or overexpression and studied the differentiation capability of adipose tissue-derived mesenchymal stem cell into adipocytes, as well as the influence on several IR-related genes: GLUT4, PPARγ, UCP1 and PAI-1. RESULTS: 1) Insulin effectively induced the expression of FOXC2 protein in adipose tissue-derived mesenchymal stem cells under differentiation (P<0.01). Insulin also induced FOXC2-pro-512T promoter activity significantly (P<0.01). 2) The stem cell adipose differentiation decreased in the FOXC2 overexpression group. 3) When FOXC2 was overexpressed, the expression of GLUT4, PAI-1 and UCP1 was higher than control groups (p<0.001). When FOXC2 was down-regulated by siRNA, both GLUT4 and PAI-1's protein expression were decreased (p<0.001), and the protein expression of PPARγ was increased (p<0.001). In the presence of insulin induction, overexpression of FOXC2 led to significantly higher UCP-1 expression (p<0.001) and lower PAI-1 expression (p<0.001). The protein expression of GLUT4, PAI-1 (p<0.001) and UCP-1 (p<0.05) was decreased in cells transfected with FOXC2 siRNA. CONCLUSION: Insulin effectively induced the expression of FOXC2 protein in adipose tissue-derived mesenchymal stem cells under differentiation, possibly through the regulation of the FOXC2-pro-512T promoter activity. The different protein expression of FOXC2 has regulatory effects on several genes related to insulin resistance. FOXC2 is an important regulatory factor in adipocyte differentiation and insulin resistance.

Perovskite nickelates as bio-electronic interfaces.

Functional interfaces between electronics and biological matter are essential to diverse fields including health sciences and bio-engineering. Here, we report the discovery of spontaneous (no external energy input) hydrogen transfer from biological glucose reactions into SmNiO3, an archetypal perovskite quantum material. The enzymatic oxidation of glucose is monitored down to ~5 × 10-16 M concentration via hydrogen transfer to the nickelate lattice. The hydrogen atoms donate electrons to the Ni d orbital and induce electron localization through strong electron correlations. By enzyme specific modification, spontaneous transfer of hydrogen from the neurotransmitter dopamine can be monitored in physiological media. We then directly interface an acute mouse brain slice onto the nickelate devices and demonstrate measurement of neurotransmitter release upon electrical stimulation of the striatum region. These results open up avenues for use of emergent physics present in quantum materials in trace detection and conveyance of bio-matter, bio-chemical sciences, and brain-machine interfaces.

Evaluation of smoking cessation intervention in patients with chronic diseases in smoking cessation clinics.

This study aimed to evaluate the effects of psychological intervention and psychological plus drug intervention on smoking cessation among male smokers with single chronic diseases.A total of 509 male smokers were divided into psychological group (n = 290) and psychological plus drugs (n = 219) groups according to their will. The physicians provided free individual counseling and follow-up interviews with brief counseling for all the subjects. In addition to mental intervention, patients in psychological plus drug group also received bupropion hydrochloride or varenicline tartrate to quit smoking. Outcomes were self-reported, regarding the 7-day point prevalence on abstinence rate and continuous abstinence rates at 1-, 3-, and 6-month follow-up period. Data analyses were performed using intention-to-treat analysis and per protocol analysis.With regards to the 3 follow-up time points, 7-day point-prevalence abstinence rate in psychological plus drugs group was all higher than that in the psychological intervention group. Additionally, the 3-month continuous abstinence rate (21.4%) of the 6-month follow-up in the psychological group was not significantly higher than that (26.9%) in the psychological plus drugs group (P >.05 for all). Fagerström test score, stage of quitting smoking, perceived confidence or difficulty in quitting, and chronic disease types were independently correlated with 3-month continuous abstinence in the 6-month follow up (P <.05 for all). The results were similar between intentional analysis and protocol analysis.The psychological intervention and psychological plus drugs intervention exerted good effects on smoking cessation in a short time (1 month). Nevertheless, the advantages did not appear during long-time (6 months) follow-up.