Using VR-based interventions, wearable technology, and text mining to improve military and Veteran mental health

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

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A lifeline in the dark: Breaking through the stigma of veteran mental health and treating America's combat veterans

Journal of Clinical Psychology ◽

10.1002/jclp.22918 ◽

2020 ◽

Vol 76 (5) ◽

pp. 831-840

Author(s):

Gabriel Botero ◽

Nilsa I. Rivera ◽

Shakeya C. Calloway ◽

Pedro L. Ortiz ◽

Emily Edwards ◽

...

Keyword(s):

Mental Health ◽

Combat Veterans ◽

Veteran Mental Health

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Best Paper Selection

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677936 ◽

2019 ◽

Vol 28 (01) ◽

pp. 179-180

Keyword(s):

Breast Cancer ◽

Mental Health ◽

Text Mining ◽

Drug Treatment ◽

Topic Model ◽

Web Based ◽

Novel Approach ◽

Health Communities ◽

Model Approach

Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018;20(3):e85 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874436/ Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer. JMIR Med Inform 2018;6(4):e45 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293240/ Park A, Conway M, Chen AT. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav 2018 Jan;78:98-112 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810583/

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Analysis of E-mental Health Research: Mapping the Relationship between Information Technology and Mental Healthcare

10.21203/rs.3.rs-741015/v1 ◽

2021 ◽

Author(s):

tatsawan timakum ◽

Min Song ◽

Qing Xie

Keyword(s):

Mental Health ◽

Information Technology ◽

Text Mining ◽

Information Extraction ◽

Health Research ◽

Health Care Services ◽

Text Message ◽

Mental Healthcare ◽

Mental Health Research ◽

Mental Wellbeing

Abstract Background: E-mentalhealthcare is the convergence of digital technologies with mental health services. It has beendevelopedto fill a gap in healthcare for people who need mental wellbeing support and may never otherwise receive psychological treatment.This study aimed to apply text mining techniques to analyze the huge data of e-mental health researches and to report on research clusters and trends as well as the co-occurrence of biomedical and the use of information technology in this field.Methods: The e-mentalhealth research data was obtainedfrom 3,663 bibliographicrecords from Web of Science (WoS)and 3,172 full-text articlesfrom PubMed Central (PMC). The text mining techniques utilized for this study includedbibliometric analysis, information extraction, and visualization.Results: The e-mental health research topic trendsprimarily involvede-health care services and medical informatics research. The clusters of research comprise 16 clusters, which refer to mental sickness, ehealth, diseases, IT, and self-management. Based onthe information extraction analysis, in the biomedical domain, a “depression” entity was frequently detected and it pairs with other entities in the network with a betweenness centrality weighted at 0.046869 (eg. depression-online, depression-diabetes, depression-measure, and depression-mobile).The IT entity-relations of “mobile” were the most frequently found(weighted at 0.043466). The top pairs are related to depression, mobile health, and text message.Conclusions: E-mental health research trends focused on disease related-depression and using IT for treatment and prevention, primarily via online and mobile devices. Producing AI and machine learning are also being studied for e-mental healthcare. The results illustrate that physical sickness is likely to cause a mental health problem and identify the IT that was applied to help manage and mitigate mental health impacts.

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Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

10.2196/preprints.11548 ◽

2018 ◽

Author(s):

George Karystianis ◽

Armita Adily ◽

Peter Schofield ◽

Lee Knight ◽

Clara Galdon ◽

...

Keyword(s):

Mental Health ◽

Domestic Violence ◽

Text Mining ◽

Large Scale ◽

New South ◽

New South Wales ◽

Mental Health Disorders ◽

Free Text ◽

South Wales ◽

Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

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Correction: Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

Journal of Medical Internet Research ◽

10.2196/13007 ◽

2019 ◽

Vol 21 (4) ◽

pp. e13007 ◽

Cited By ~ 2

Author(s):

George Karystianis ◽

Armita Adily ◽

Peter Schofield ◽

Lee Knight ◽

Clara Galdon ◽

...

Keyword(s):

Mental Health ◽

Domestic Violence ◽

Text Mining ◽

Mental Health Disorders ◽

Automatic Extraction ◽

Health Disorders

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Heart Rate Variability Duration: Expanding the Ability of Wearable Technology to Improve Outpatient Monitoring?

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.682553 ◽

2021 ◽

Vol 12 ◽

Author(s):

David C. Sheridan ◽

Karyssa N. Domingo ◽

Ryan Dehart ◽

Steven D. Baker

Keyword(s):

Mental Health ◽

Heart Rate ◽

Heart Rate Variability ◽

Time Windows ◽

Low Frequency ◽

Frequency Component ◽

Wearable Technology ◽

Mental Health Research ◽

High Frequency Component ◽

Frequency Domains

Heart rate variability (HRV) evaluates beat-to-beat interval (BBI) differences and is a suggested marker of the autonomic nervous system with diagnostic/monitoring capabilities in mental health; especially parasympathetic measures. The standard duration for short-term HRV analysis ranges from 24 h down to 5-min. However, wearable technology, mainly wrist devices, have large amounts of motion at times resulting in need for shorter duration of monitoring. The objective of this study was to evaluate the correlation between 1 and 5 min segments of continuous HRV data collected simultaneously on the same patient. Subjects wore a patch electrocardiograph (Cardea Solo, Inc.) over a 1–7 day period. For every consecutive hour the patch was worn, we selected a 5-min, artifact-free electrocardiogram segment. HRV metric calculation was performed to the entire 5-min segment and the first 1-min from this same 5-min segment. There were 492 h of electrocardiogram data collected allowing calculation of 492 5 min and 1 min segments. 1 min segments of data showed good correlation to 5 min segments in both time and frequency domains: root mean square of successive difference (RMSSD) (R = 0.92), high frequency component (HF) (R = 0.90), low frequency component (LF) (R = 0.71), and standard deviation of NN intervals (SDNN) (R = 0.63). Mental health research focused on parasympathetic HRV metrics, HF and RMSSD, may be accomplished through smaller time windows of recording, making wearable technology possible for monitoring.

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