Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

This survey study assesses the rates of abuse, neglect, and bullying experienced at home or in school by transgender and gender-nonbinary adolescents in China and the associations of these experiences with mental health.

No abstract

Background China has the world’s largest lesbian, gay, bisexual, and transgender (LGBT) population. This study assessed the discrimination experienced by LGBT individuals in China in a comprehensive way, covering discrimination perpetrated by family, media, medical services, religious communities, schools, social services, and in the workplace. Methods The current study involved a national survey of 31 provinces and autonomous regions. Discrimination was measured both in terms of heterosexual participants’ attitudes towards LGBT individuals, and LGBT participants’ self-perceived discrimination. Pearson correlation analysis was performed to examine the difference between heterosexual participants’ attitudes towards LGBT individuals and LGBT participants’ self-perceived discrimination. Linear regression was used to investigate the association between gross domestic product per capita and discrimination. Results Among 29,125 participants, 2066 (7.1%) identified as lesbian, 9491 (32.6%) as gay, 3441 (11.8%) as bisexual, 3195 (11.0%) as transgender, and 10,932 (37.5%) as heterosexual. Heterosexual people were generally friendly towards the LGBT community with a mean score of 21.9 (SD = 2.7, total scale score = 100) and the grand averaged score of self-perceived discrimination by LGBT participants was 49.9 (SD = 2.5). Self-perceived discrimination from family and social services is particularly severe. We created a series of provincial level choropleth maps showing heterosexual participants’ acceptance towards the LGBT community, and self-perceived discrimination reported by members of the LGBT community. We found that a higher level of economic development in provinces was associated with a decrease in discrimination, and we identified that every 100 thousand RMB increase in per capita GDP lead to a 6.4% decrease in discriminatory events perpetrated by heterosexuals. Conclusions Chinese LGBT groups consistently experience discrimination in various aspects of their daily lives. The prevalence of this discrimination is associated with the economic development of the province in which it occurs. In order to reduce discrimination, it is important for future studies to discover the underlying reasons for discrimination against LGBT individuals in China.

This preliminary evidence suggests that short, group-based psychoeducation benefits currently medicated inpatients following the remission of mania in bipolar I disorder. This intervention warrants further investigation, especially in other Chinese populations. If future studies confirm its benefits, group-based psychoeducation could be incorporated into routine psychiatric inpatient care for bipolar patients in China.

Background Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. Objective The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. Methods We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients’ Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173

No abstract

Purpose: Smartphone-based questionnaires have advantages compared with their paper versions, but there is a lack of consistent research on depressive disorder questionnaires. This study aimed to assess the equivalence between the paper and smartphone versions of the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16) and Patient Health Questionnaire-9 (PHQ-9) for patients with depressive disorders in psychiatric hospitals in China. Patients and Methods: This was a randomized crossover study of 110 depressed patients recruited from the outpatient department of Beijing Anding Hospital from March 2016 to September 2018. Group 1 completed both the QIDS-SR16 and PHQ-9 in paper format and then completed the smartphone version 1-2 h later. Group 2 completed the scales in the reverse order. Reliability was evaluated using intraclass correlation coefficients (ICCs) with 95% confidence intervals (CI). The expected ICC was 0.9 (α=0.05). Results: The overall ICC score of the QIDS-SR16 paper and smartphone versions was 0.904 (95% CI: 0.861-0.934), and the ICCs of each item ranged from 0.769 to 0.923. The overall ICC score of the PHQ-9 paper and smartphone versions was 0.951 (95% CI: 0.929-0.967), and the ICCs of each item ranged from 0.779 to 0.914. Conclusion: This study demonstrated the equivalence of the paper and smartphone versions of the PHQ-9 and QIDS-SR16 in depressed patients in China.