scholarly journals Association of Depression and Anxiety with Social Network Types: Results from a Community Cohort Study

2021 ◽  
Vol 18 (11) ◽  
pp. 6120
Author(s):  
Saju Madavanakadu Devassy ◽  
Lorane Scaria ◽  
Natania Cheguvera ◽  
Kiran Thampi
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Assessment Tools ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Lower Odds ◽  
Mental Health Conditions ◽  
Catchment Areas ◽  
Network Type

Social networks protect individuals from mental health conditions of depression and anxiety. The association between each social network type and its mental health implications in the Indian population remains unclear. The study aims to determine the association of depression and anxiety with different social network types in the participants of a community cohort. We conducted a cross-sectional household survey among people aged ≥30 years in geographically defined catchment areas of Kerala, India. We used cross-culturally validated assessment tools to measure depression, anxiety and social networks. An educated male belonging to higher income quartiles, without any disability, within a family dependent network has lower odds of depression and anxiety. Furthermore, 28, 26.8, 25.7, 9.8 and 9.7% of participants belonged to private restricted, locally integrated, wider community-focused, family-dependent and locally self-contained networks, respectively. Close ties with family, neighbours, and community had significantly lower odds of anxiety and depression than private restricted networks. The clustering of people to each social network type and its associated mental health conditions can inform social network-based public health interventions to optimize positive health outcomes in the community cohort.

scholarly journals Heterogeneous Network Approach to Predict Individuals’ Mental Health

2021 ◽  
Vol 15 (2) ◽  
pp. 1-26
Author(s):  
Shikang Liu ◽  
Fatemeh Vahedian ◽  
David Hachen ◽  
Omar Lizardo ◽  
Christian Poellabauer ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Health States ◽  
Mental Health Conditions ◽  
Health Related ◽  
Types Of Information ◽  
The University ◽  
Critical Public Health

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.

Virtual Reality for Supporting Depression and Anxiety: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Nilufar Baghaei ◽  
Vibhav Chitale ◽  
Andrej Hlasnik ◽  
Lehan Stemmet ◽  
Hai-Ning Liang ◽  
...  
Keyword(s):  
Mental Health ◽  
Virtual Reality ◽  
Virtual Environment ◽  
Scoping Review ◽  
Google Scholar ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Virtual Reality Exposure Therapy ◽  
Mental Health Conditions ◽  
Virtual Reality Exposure

BACKGROUND Mental health conditions pose a major challenge to healthcare providers and society at large. The World Health Organization (WHO) predicts that by 2030, mental health conditions will be the leading disease burden globally. The current need for mental health care is overwhelming. In New Zealand, one in six adults have been diagnosed with common mental disorders such as depression, and anxiety disorders according to a national survey. Cognitive behavioral therapy (CBT) has been shown to effectively help patients overcome a wide variety of mental health conditions. Virtual Reality Exposure Therapy (VRET) might be one of the most exciting technology that is emerging in the clinical setting for the treatment of anxiety and depression. OBJECTIVE This study aimed to investigate what VR technologies are currently being used to help suppress depression and anxiety. Primarily we identified whether the CBT was included as part of the virtual reality exposure therapy treatment (VRET), and if so, how? Equally important, the focus was set not only on VR hardware and used software tools but also on what the participants did in the virtual environment and how the virtual environment looked like METHODS We performed a scoping review. To identify significant studies, we decided to use already aggregated sources in Google Scholar Database. Overall, the goal of our search strategy was to limit the number of initial results related to virtual reality in mental health to only a relevant minimum. RESULTS Using our defined key words, Google Scholar identified more than 17300 articles. After applying all inclusion and exclusion criteria, we identified a total of 369 articles for further processing. After manual evaluation, 34 articles were shortlisted, of which 9 reported the usage of CBT with VR. All these articles were published between 2017 and 2021. CONCLUSIONS Majority of the studies demonstrated the use of VR to be effective for suppressing anxiety or depression in a range of settings and recommended its potential as tool for usage in a clinical environment. As standalone headsets are much easier to work with and more suitable for home usage, the shift from tethered VR headsets to standalone headsets in the mental health environment was not observed. A total of 9 studies explicitly mentioned the usage of CBT. Out of these, CBT was conducted within a virtual reality environment in 5 studies while in the remaining 4 studies CBT was used as an addition to VRET. All 9 studies reported the use of CBT either in vivo or inside a virtual environment to be effective in suppressing anxiety or depression.

70-LB: Fat Mass and Obesity-Associated rs9939609 Gene Variant Is Related to Fasting Glucose in Children with Mental Health Conditions

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 70-LB
Author(s):  
ALEJANDRA M. WIEDEMAN ◽  
YING FAI NGAI ◽  
AMANDA M. HENDERSON ◽  
CONSTADINA PANAGIOTOPOULOS ◽  
ANGELA M. DEVLIN
Keyword(s):  
Mental Health ◽  
Fat Mass ◽  
Fasting Glucose ◽  
Health Conditions ◽  
Gene Variant ◽  
Mental Health Conditions

Mental health recovery and its correlates: Perspectives from 2 countries (Preprint)

2020 ◽  
Author(s):  
Huiting Xie
Keyword(s):  
Mental Health ◽  
United States ◽  
Self Efficacy ◽  
The United States ◽  
Mental Illnesses ◽  
Health Conditions ◽  
Mental Health Recovery ◽  
Mental Health Conditions ◽  
Serious Mental Illnesses ◽  
Promote Mental Health

BACKGROUND Many people are affected by mental health conditions, yet its prevalence in certain populations are not well documented. OBJECTIVE The aim of this study is to describe the attributes of people with mental health conditions in U.S and SG in terms of: perception of mental health recovery and its correlates such as strengths self-efficacy, resourcefulness and stigma experience. With the findings, not only could the knowledge base for mental health recovery in both countries be enhanced but interventions and policies relating to self-efficacy, resourcefulness and de-stigmatization for mental health recovery could be informed. METHODS A A cross-sectional, descriptive study with convenience sample of 200 community dwelling adults were selected, 100 pax from the United States (U.S) and 100 pax from Singapore (SG). Adults with serious mental illnesses without substance abuse impacting on their recovery were recruited. Participants completed self-administered questionaires measuring their mental health recovery, strengths self-efficacy, resourcefulness and stigma experience. RESULTS This study offered the unique opportunity to examine mental health recovery as well as its correlates such as strengths self-efficacy, resourcefulness and stigma experience from both the United States and Singapore. While the perception of mental health recovery and positive attributes like strengths self-efficacy and resourcefulness remained strong in participants with serious mental illnesses across both countries, people with serious mental illnesses in both countries still experienced negative perception like stigma. The findings would not only inform strategies to promote mental health recovery but also enhance the focus on correlates such as strengths self-efficacy and resourcefulness across both countries. CONCLUSIONS The findings would not only inform strategies to promote mental health recovery but also enhance the focus on correlates such as strengths self-efficacy and resourcefulness across both countries.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

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