Psychometric properties of the ultra-brief self-report Patient Health Questionnaire-4 (PHQ-4) to assess anxiety and depression in Arabic-speaking adults.

BACKGROUND: Anxiety and depression are psychiatric disorders that often coexist and share some features. Developing a simple and cost-effective tool to assess anxiety and depression in the Arabic-speaking population, predominantly residing in low- and middle-income nations where research can be arduous, would be immensely beneficial. The study aimed to translate the four-item composite Patient Health Questionnaire - 4 (PHQ-4) into Arabic and evaluate its psychometric properties, including internal reliability, sex invariance, composite reliability, and correlation with measures of psychological distress. METHODS: 587 Arabic-speaking adults were recruited between February and March 2023. An anonymous self-administered Google Forms link was distributed via social media networks. We utilized the FACTOR software to explore the factor structure of the Arabic PHQ-4. RESULTS: Confirmatory factor analysis (CFA) indicated that fit of the two-factor model of the PHQ-4 scores was modest (χ2/df = .13/1 = .13, RMSEA = .001, SRMR = .002, CFI = 1.005, TLI = 1.000). Internal reliability was excellent (McDonald's omega = .86; Cronbach's alpha = .86). Indices suggested that configural, metric, and scalar invariance were supported across sex. No significant difference was found between males and females in terms of the PHQ-4 total scores, PHQ-4 anxiety scores, and PHQ-4 depression scores. The total score of the PHQ-4 and its depression and anxiety scores were significantly and moderately-to-strongly associated with lower wellbeing and higher Depression Anxiety and Stress Scale (DASS) total and subscales scores. CONCLUSION: The PHQ-4 proves to be a reliable, valid, and cost-effective tool for assessing symptoms related to depression and anxiety. To evaluate the practical effectiveness of the Arabic PHQ-4 and to further enhance the data on its construct validity, future studies should assess the measure in diverse contexts and among specific populations.

A longitudinal examination of the measurement properties and invariance of the Sleep Condition Indicator in Chinese healthcare students.

BACKGROUND: The Sleep Condition Indicator (SCI), an insomnia measurement tool based on the updated Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria with sound psychometric properties when applied in various populations, was evaluated here among healthcare students longitudinally, to demonstrate its measurement properties and invariance in this particularly high-risk population. METHODS: Healthcare students of a Chinese university were recruited into this two-wave longitudinal study, completing the simplified Chinese version of the SCI (SCI-SC), Chinese Regularity, Satisfaction, Alertness, Timing, Efficiency, Duration (RU_SATED-C) scale, Chinese Patient Health Questionnaire-4 (PHQ-4-C), and sociodemographic variables questionnaire (Q-SV) between September and November 2022. Structural validity, measurement invariance (MI), convergent and discriminant validity, internal consistency, and test-retest reliability of the SCI-SC were examined. Subgroups of gender, age, home location, part-time job, physical exercise, and stress-coping strategy were surveyed twice to test cross-sectional and longitudinal MI. RESULTS: We identified 343 valid responses (62.9% female, mean age = 19.650 ± 1.414 years) with a time interval of seven days. The two-factor structure was considered satisfactory (comparative fit index = 0.953-0.989, Tucker-Lewis index = 0.931-0.984, root means square error of approximation = 0.040-0.092, standardized root mean square residual = 0.039-0.054), which mostly endorsed strict invariance except for part-time job subgroups, hence establishing longitudinal invariance. The SCI-SC presented acceptable convergent validity with the RU_SATED-C scale (r ≥ 0.500), discriminant validity with the PHQ-4-C (0.300 ≤ r < 0.500), internal consistency (Cronbach's alpha = 0.811-0.835, McDonald's omega = 0.805-0.832), and test-retest reliability (intraclass correlation coefficient = 0.829). CONCLUSION: The SCI-SC is an appropriate screening instrument available for assessing insomnia symptoms among healthcare students, and the promising measurement properties provide additional evidence about validity and reliability for detecting insomnia in healthcare students.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network.

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

Assessing psychometric properties and measurement invariance of the Sleep Quality Questionnaire among healthcare students.

OBJECTIVE: The sleep of healthcare students is worth discovering. Mental health and self-rated health are thought to be associated with sleep quality. As such, valid instruments to assess sleep quality in healthcare students are crucial and irreplaceable. This study aimed to investigate the measurement properties of the Sleep Quality Questionnaire (SQQ) for Chinese healthcare students. METHODS: Two longitudinal assessments were undertaken among healthcare students, with a total of 595, between December 2020 and January 2021. Measures include the Chinese version of the SQQ, Patient Health Questionnaire-4 (PHQ-4), Self-Rated Health Questionnaire (SRHQ), and sociodemographic questionnaire. Structural validity through confirmatory factor analysis (CFA) was conducted to examine factor structure of the SQQ. T-tests and ANOVAs were used to examine sociodemographic differences in sleep quality scores. Multi Group CFA and longitudinal CFA were respectively used to assess cross-sectional invariance and longitudinal invariance across two-time interval, i.e., cross-cultural validity. Construct validity, internal consistency, and test-retest reliability were correspondingly examined via Spearman correlation, Cronbach's alpha and McDonald's omega, and intraclass correlation coefficient. Multiple linear regression analysis was performed to examine incremental validity of the SQQ based on the PHQ-4 and SRHQ as indicators of the criterion variables. RESULTS: CFA results suggested that the two-factor model of the SQQ-9 (item 2 excluded) had the best fit. The SQQ-9 scores differed significantly by age, grade, academic stage, hobby, stress coping strategy, anxiety, depression, and self-rated health subgroups. Measurement invariance was supported in terms of aforesaid subgroups and across two time intervals. In correlation and regression analyses, anxiety, depression, and self-rated health were moderately strong predictors of sleep quality. The SQQ-9 had good internal consistency and test-retest reliability. CONCLUSION: Good measurement properties suggest that the SQQ is a promising and practical measurement instrument for assessing sleep quality of Chinese healthcare students.