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 - 02 February 2023

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 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 - 20 January 2023

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 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 - 05 January 2023

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… 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 - 05 January 2023

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… 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 - 28 December 2022

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… 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 - 29 November 2022

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… More >

A. Couillet, B. Mastroianni, J. Hailloud, M.-P. Le Bris, G. Chvetzoff

Psycho-Oncologie, Vol.16, No.1, pp. 182-191, 2022, DOI:10.3166/pson-2022-0183

Abstract Des ateliers de méditation ont été proposés aux patients, nous en avons étudié la faisabilité pendant les soins oncologiques. Ils ont été adaptés du fait de la Covid-19 : une session a eu lieu en présentiel, la suivante en distanciel. Les données ont été analysées de manière rétrospective. Une analyse mixte a été menée : le volet quantitatif a évalué la participation aux ateliers, les caractéristiques des patients et l’impact des ateliers, le volet qualitatif s’est concentré sur l’appropriation de cet outil par les patients. Concernant la faisabilité, 66,7 % des patients ont terminé le 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 - 28 September 2022

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), 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 - 06 September 2022

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… More > Graphic Abstract

Gabriela Nazar^1,2,*, Carlos-María Alcover³, Yeny Concha-Cisternas^4,5, Igor Cigarroa⁵, Ximena Díaz-Martínez⁶, Mariela Gatica-Saavedra⁷, Fabián Lanuza^8,9, Ana María Leiva-Ordónez¹⁰, María Adela Martínez-Sanguinetti¹¹, Miquel Martorell^2,12, Fanny Petermann-Rocha^13,14, Claudia Troncoso-Pantoja¹⁵, Carlos Celis-Morales¹⁶

International Journal of Mental Health Promotion, Vol.24, No.5, pp. 679-697, 2022, DOI:10.32604/ijmhp.2022.020105 - 27 July 2022

Abstract Depressive disorders are recognized as one of the most common mental health conditions across different age groups. However, the risk factors associated with depression among older people from low-and middle-income countries remains unclear. This study aims to identify socio-demographic, health and psychosocial-related factors associated with depression in Chilean older adults. A cross-sectional study was carried out in a representative sample of 1,765 adults aged ≥60 years participants from the Chilean National Health Survey 2016–2017. Depression was assessed with the Composite International Diagnostic Interview (CIDI-SF). Associations between the exposure variables and depression were investigated using Poisson… More >