Roza Zadeh^#, Jigar Jogia^*

International Journal of Mental Health Promotion, Vol.25, No.3, pp. 309-326, 2023, DOI:10.32604/ijmhp.2023.022491

Abstract There are over thirty million refugees globally with severe experiences of trauma. Art therapy intervention allows for nonverbal expression and could alleviate mental health symptomatology among refugees. The present review’s aim was to integrate and summarize the previous research which examined the effects of visual arts on alleviating psychological conditions of refugees. However, due to the paucity of studies which solely used visual arts, we included studies that used visual arts alongside other modalities as part of an expressive arts therapy intervention. The present review synthesizes studies that examined the effect of art therapy on mental health issues of refugees… More >

Zhendong Wu¹, Chen Xu², Liyan Zhang³, Yang Wang⁴, George W. Leeson⁵, Gong Chen^4,*, Julien S. Baker⁶, Xiao-Guang Yue^7,8

International Journal of Mental Health Promotion, Vol.25, No.3, pp. 403-419, 2023, DOI:10.32604/ijmhp.2023.024638

Abstract Introduction:: Older adults are prone to high levels of depression due to their deteriorating physical functions and shrinking social networks after retirement. Volunteering as an important social activity is essential for alleviating depression by building social network. This paper aims to examine the effect of volunteering on depression among older adults by using China Longitudinal Aging Social Survey (CLASS 2018) data.Methods:: This study uses descriptive analysis and chi-square tests to show differences in demographic factors of older adults’ volunteerism participation, followed by bivariate correlation analysis to examine the correlation between the vital variables. Afterward, stratified linear regression analysis is used… More >

Jingjing Wang¹, Xiangli Guan^1,*, Yue Zhang², Yang Li¹, Md Zahir Ahmed³, Mary C. Jobe⁴, Oli Ahmed⁵

International Journal of Mental Health Promotion, Vol.25, No.2, pp. 223-235, 2023, DOI:10.32604/ijmhp.2022.025570

Abstract Individuals’ perceptions, attitudes, and patterns of getting along with family members are important factors influencing Chinese people’s self-evaluation. The aim of this study was to investigate the effect of family cohesion on depression and the role of perceived social support and intentional self-regulation in this association. A hypothesized model of the association of family cohesion, perceived social support, intentional self-regulation, and depression was examined. A convenience sampling method was used to survey 1,180 college students in Yunnan Province using self-report. Data were collected using the Family Cohesion Scale, the Perceived Social Support Scale, the Intentional Self-Regulation Scale, and the Center… More >

Ali Alqazzaz¹, Mohammad Tabrez Quasim^1,*, Mohammed Mujib Alshahrani¹, Ibrahim Alrashdi², Mohammad Ayoub Khan¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 551-562, 2023, DOI:10.32604/csse.2023.031048

Abstract Facebook, Twitter, Instagram, and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts, posts, comments, images, and videos that express moods, sentiments, and feelings. With this, it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes. However, the analysis of depression based on social media has gained widespread acceptance worldwide, other verticals still have yet to be discovered. The depression analysis uses Twitter data from a publicly available web source in this work. To assess the accuracy of depression detection, long-short-term memory (LSTM)… More >

Doaa Sami Khafaga¹, Maheshwari Auvdaiappan², K. Deepa³, Mohamed Abouhawwash^4,5, Faten Khalid Karim^1,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1301-1313, 2023, DOI:10.32604/iasc.2023.033360

Abstract Today social media became a communication line among people to share their happiness, sadness, and anger with their end-users. It is necessary to know people’s emotions are very important to identify depressed people from their messages. Early depression detection helps to save people’s lives and other dangerous mental diseases. There are many intelligent algorithms for predicting depression with high accuracy, but they lack the definition of such cases. Several machine learning methods help to identify depressed people. But the accuracy of existing methods was not satisfactory. To overcome this issue, the deep learning method is used in the proposed method… More >

S. Kavi Priya^*, K. Pon Karthika

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory… More >

Ayan Kumar¹, Abdul Quadir Md¹, J. Christy Jackson^1,*, Celestine Iwendi²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.033431

Abstract In today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which can share data with a… More >

Weiman Kong¹, Jiayi Gu^2,*

International Journal of Mental Health Promotion, Vol.25, No.1, pp. 139-152, 2023, DOI:10.32604/ijmhp.2022.022123

Abstract This study aimed to examine the associations between lifestyle behaviors and depressive symptoms in adolescents. Self-reported data from the 2019 Youth Risk Behavior Survey (YRBS) was analyzed. Depressive symptoms were set as the outcome variable. Movement variables (physical activity, muscle-strengthening exercise, physical education attendance, sports team participation, television watching, video or computer games, and sleep), eating behaviors (fruit intake, vegetable intake, milk intake, and eating breakfast or not), and substance use (alcohol use and cigarette use) were included as explanatory variables. Binary logistic regression was used to explore the associations between lifestyle behaviors and depressive symptoms after adjusting for sex,… More >

Xiying Li¹, Alyx Taylor², Jinming Li³, Ting Wang³, Jing Kuang³, Zhihao Zhang³, Xiaolei Liu⁴, Tingting Liu⁴, Xia Qin⁵, Shenghua Lu^6,7,*, Liye Zou³

International Journal of Mental Health Promotion, Vol.24, No.6, pp. 855-867, 2022, DOI:10.32604/ijmhp.2022.021508

Abstract Objective: This study explored the effects of Health Qigong exercise on depression and anxiety in patients with Parkinson’s disease (PD). Methods: A total of 42 volunteers who met the inclusion criteria were recruited and randomly allocated into the experimental group and the control group. The experimental group carried out 60-minute sessions of Health Qigong exercise five times a week for 12 weeks while the control group did not perform any regular physical exercise. Data on cognitive impairment, psychomotor retardation, somatic anxiety, weight loss and sleep disorders, the sum score of the 17-item Hamilton Depression Rating Scale (HDRS-17), state anxiety, trait… More >

Jodi L. Feinberg¹, Peter Sheng², Stephanie Pena², Adam J. Small¹, Susanna Wendelboe¹, Katlyn Nemani³, Vikram Agrawal⁴, Dan G. Halpern^1,*

Congenital Heart Disease, Vol.17, No.5, pp. 519-531, 2022, DOI:10.32604/chd.2022.024174

Abstract Background: Adults with congenital heart disease (ACHD) have increased prevalence of mood and anxiety disorders. There are limited data regarding the influence of the COVID-19 pandemic on the mental health and health behaviors of these patients. Objective: The purpose is to evaluate the perceptions, emotions, and health behaviors of ACHD patients during the COVID-19 pandemic. Methods: In this cross-sectional study of ACHD patients, we administered surveys evaluating self-reported emotions, perceptions and health behaviors. Logistic regressions were performed to determine the adjusted odds of displaying each perception, emotion and health behavior based on predictor variables. Results: Ninety-seven patients (mean age 38.3… More > Graphic Abstract