Logistic Regression on Factors Affecting Depression among the Elderly

Wahyuningsih, Christiana Sri; Subijanto, Achmad Arman; Murti, Bhisma

doi:10.26911/jepublichealth.2019.04.03.03

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The lack of physical contact with their loved ones as well as fears caused by uncertainty and the virus placed elderly people in the context of relative social isolation with a negative impact on their well-being. Nevertheless, social isolation and loneliness increase the risk of suffering from mental disorders such as depression [ 5 , 6 , 7 , 9 , 10 ], study participants did not develop any of those extreme conditions. This finding can be explained by the age of the participants in this study.…”

Section: Discussionmentioning

confidence: 99%

“…The linkages between social isolation, loneliness and both physical and mental health have significantly increased the number of studies since 2000 [ 5 ], as a result of the concern within the societies with rapidly aging populations [ 3 , 4 ]. Social isolation and loneliness have particularly detrimental effects on the physical and mental health of elderly people [ 5 , 6 , 7 ], affecting from one third to half of this social group [ 3 , 8 ]. Both the perception of social isolation linked to the perception of loneliness and the feeling of lack of social and family support linked to rare interactions and limited social networks lead to affective disorders fostering or worsening symptoms of anxiety and depression in elderly people [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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The Enabling Role of ICT to Mitigate the Negative Effects of Emotional and Social Loneliness of the Elderly during COVID-19 Pandemic

Barroso

Kolotouchkina

Viniegra

2021

IJERPH

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(1) Background: The COVID-19 pandemic has been especially hard on the elderly owing to their particular vulnerability to the virus. Their confinement to prevent the spread of the virus resulted in social isolation, often linked to the unwanted loneliness that hinders their emotional well-being. The enabling capacity of ICT (Information and Communication Technology) to overcome the negative effects of this isolation requires special attention. The purpose of this research is to understand the impact of the use of ICT on the emotional well-being of elderly people during their confinement. (2) Methods: A qualitative exploration method based on four focus groups with elderly people aged 60 years or older and three in-depth personal interviews with experts in education of the elderly were carried out. (3) Results: Research results evidence a negative emotional impact of the confinement (lack of physical contact with their loved ones, fear and uncertainty, feeling of loneliness, sadness at the loss of family members) on the emotional well-being of study participants. Furthermore, the operational capacity of ICT to prevent infection, as well as their positive emotional and humanizing role in providing access to entertainment and hobbies, and in improving self-esteem was also acknowledged. (4) Conclusions: ICT have become a valuable ally for elderly people aged 60 years and older to mitigate the negative effects of social isolation and loneliness imposed by the confinement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Enabling Role of ICT to Mitigate the Negative Effects of Emotional and Social Loneliness of the Elderly during COVID-19 Pandemic

Barroso

Kolotouchkina

Viniegra

2021

IJERPH

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“…4)Logistic Regression [18]: A binary classification algorithm that estimates the probability of a binary outcome based on one or more predictor variables. It models the relationship between the independent variables and the categorical dependent variable using a logistic function.…”

Section: Results From Other Traditional ML Modelsmentioning

confidence: 99%

BXCNN: A novel depression detection model

Xi,

Zhong,

Wang

2024

ACE

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In response to the mounting societal pressures, an increasing number of individuals grappling with mental health challenges are turning to social media platforms to express their feelings. The utilization of deep learning models for analyzing social media data has become increasingly crucial in detecting early signs of depression. Early intervention through depression detection can significantly enhance patients quality of life and even save lives. However, many existing deep learning models suffer from low prediction accuracy, exacerbated by the imbalance between positive and negative samples in the collected data. To address these challenges, we propose a novel depression detection model integrated with BERT, XGBoost, and Convolutional Neural Networks (BXCNN). This model harnesses the advantages of ensemble learning and deep learning technologies by integrating XGBoost for feature extraction to alleviate data imbalance and CNN for classification. We transform depression-related textual data into sentence vectors using BERT to capture semantic information effectively. These features are then fed into a CNN classifier to accurately predict the likelihood of individuals exhibiting depressive symptoms. Through empirical evaluations on relevant datasets, our approach excels across various evaluation metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC).

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“…A study by Wahyuningsih et al (2019) showed that there was a statistically significant effect of loneliness on the depression among elders. Lonely elders were apt to suffer depression than those who were not lonely.…”

Section: Effect Of Grief and Loss On Neuropsychiatric Disordersmentioning

confidence: 99%

The Effect of Organophosphate Pesticides Exposure and Other Factors Associated with Neuropsychiatric Disorders among Rice Farmers: A Path Analysis Evidence from Sukoharjo, Central Java

Fitriyani

Rahardjo

Murti

2020

J EPIDEMIOL PUBLIC HEALT

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Background: Pesticide is a cheap and effective substance to eradicate pests, yet it has chronic effects, it causes neuropsychiatric disorders. Neuropsychiatric disorders are associated with affecttive, cognitive, and behavioral disorders. This study aims to analyze the effects of pesticide exposure and psychosocial determinants on neuropsychiatric disorders (depression and anxiety). Subjects and Method: This was a retrospective cohort study conducted in Sukoharjo, Central Java, Indonesia. A sample of 200 rice farmers was selected by fixed exposure sampling. The dependent variable was depression nd anxiety. The independent variables were organophosphate pesticide exposure and psychosocial factors. The neuropsychiatric disorders were measured using DASS-42 questionnaires. The data were analyzed by path analysis. Results: Depression and anxiety were directly increased by high work stress (b= 0.29; 95% CI= 0.19 to 0.38; p< 0.001), depression history of family members (b= 5.71; 95% CI= 3.16 to 8.26; p<0.001), grief and loss (b= 3.21; 95% CI= 0.99 to 5.42; p= 0.005), and past trauma (b= 4.08; 95% CI= 1.81 to 6.34; p<0.001). Depression and anxiety were indirectly affected by the organophosphate pesticide exposure, age through the use of personal protective equipment (PPE), and latest education through the use of PPE. Conclusion: Depression and anxiety are directly increased by high work stress, depression history of family members, grief and loss, and past trauma. Depression and anxiety are indirectly affected by the organophosphate pesticide exposure, age through the use of personal protective equipment (PPE), and latest education through the use of PPE.

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Logistic Regression on Factors Affecting Depression among the Elderly

Cited by 5 publications

References 14 publications

The Enabling Role of ICT to Mitigate the Negative Effects of Emotional and Social Loneliness of the Elderly during COVID-19 Pandemic

The Enabling Role of ICT to Mitigate the Negative Effects of Emotional and Social Loneliness of the Elderly during COVID-19 Pandemic

BXCNN: A novel depression detection model

The Effect of Organophosphate Pesticides Exposure and Other Factors Associated with Neuropsychiatric Disorders among Rice Farmers: A Path Analysis Evidence from Sukoharjo, Central Java

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