scholarly journals Optimizing existing mental health screening methods in a dementia screening and risk-factor app: An Observational, machine learning study (Preprint)

10.2196/31209 ◽  
2021 ◽  
Author(s):  
Narayan Kuleindiren ◽  
Raphael Paul Rifkin-Zybutz ◽  
Monika Johal ◽  
Hamzah Selim ◽  
Itai Palmon ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factor ◽  
Health Screening ◽  
Mental Health Screening ◽  
Screening Methods ◽  
Learning Study ◽  
Dementia Screening

Optimizing existing mental health screening methods in a dementia screening and risk-factor app: An Observational, machine learning study (Preprint)

2021 ◽  
Author(s):  
Narayan Kuleindiren ◽  
Raphael Paul Rifkin-Zybutz ◽  
Monika Johal ◽  
Hamza Selim ◽  
Itai Palmon ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Regression Models ◽  
Superior Performance ◽  
Training Dataset ◽  
Screening Methods ◽  
Depression And Anxiety ◽  
Dementia Screening ◽  
Fold Cross Validation

BACKGROUND Mindset4Dementia is an app that aims to improve dementia screening by assessing cognition and risk factors. It considers important clinical risk factors, including prodromal symptoms, mental health disorders, and differential diagnoses of dementia. The PHQ-9 and GAD-7 are widely validated, and commonly used scales used in screening for depression and anxiety disorders respectively. Shortened versions of both (PHQ-2/GAD-2) have been produced. OBJECTIVE We sought to develop a method that maintained the brevity of these shorter questionnaires while maintaining the better precision of the original questionnaires METHODS Single questions were designed to encompass symptoms covered in the original questionnaires. Answers to these questions were combined with the PHQ-2/GAD-2 and anonymized risk factors collected by Mindset4Dementia. Machine learning models were trained to use these single questions in combination with data already collected by the app - age, response to a joke and reporting of functional impairment to predict binary and continuous outcomes as measured by the PHQ-9/GAD-7. Our model was developed with a training dataset using ten-fold cross-validation and a hold-out testing datasets and compared to results from using the shorter questionnaires (PHQ-2/GAD-2) alone to benchmark performance. RESULTS We were able to achieve superior performance in predicting PHQ-9/GAD-7 screening cut-offs than the PHQ-2 (difference In AUC 0.04, 95% CI 0.00 – 0.08, P = 0.02) but not to GAD-2 (difference in AUC 0.00, 95% CI -0.02 – 0.03, P = 0.42). In regression models we were able to accurately predict total questionnaire scores; PHQ-9 (R2 = 0.655, MAE = 2.267), GAD-7 (R2 = 0.837, MAE = 1.780). CONCLUSIONS We have developed a short screening tool for affective disorders with superior or equivalent performance to well established methods.

scholarly journals Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review

10.2196/22755 ◽  
2020 ◽  
Vol 4 (12) ◽  
pp. e22755
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Hybrid Model ◽  
Mental Health Problems ◽  
Health Screening ◽  
Mental Health Screening ◽  
Model Of Care ◽  
Psychological Screening ◽  
Prevention And Intervention ◽  
Digital Platforms

Background There is a persistent need for mental ill-health prevention and intervention among at-risk and vulnerable subpopulations. Major disruptions to life, such as the COVID-19 pandemic, present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an underrecognition of mental ill-health, and engagement difficulties that have hindered research, prevention, and intervention efforts. Objective The aims of this paper are to summarize and evaluate the literature on athletes’ increased vulnerability to mental ill-health and digital mental health solutions as a complement to prevention and intervention, and to show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking, and faster and better treatment algorithms. Methods This mini review shapes literature in the fields of athlete mental health and digital mental health by summarizing and evaluating journal and review articles drawn from PubMed Central and the Directory of Open Access Journals. Results Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence to the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning, and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for use in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing, digital mental health screening, and tracking tools have the potential to drive a consolidated, measurable, and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health. Conclusions Athletes are an at-risk subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience that helped them to positively adjust after a period of overwhelming stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health, especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely used among general populations, but there is yet to be an eminent athlete version. Sports in combination with mental health education should address the barriers to help-seeking by increasing awareness, from mental ill-health to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies, that may be used in prevention and early intervention strategies.

scholarly journals Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review (Preprint)

2020 ◽  
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Hybrid Model ◽  
Mental Health Problems ◽  
Health Screening ◽  
Mental Health Screening ◽  
Model Of Care ◽  
Psychological Screening ◽  
Prevention And Intervention ◽  
Digital Platforms

BACKGROUND There is a persistent need for mental ill-health prevention and intervention among <i>at-risk</i> and vulnerable subpopulations. Major disruptions to life, such as the COVID-19 pandemic, present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an underrecognition of mental ill-health, and engagement difficulties that have hindered research, prevention, and intervention efforts. OBJECTIVE The aims of this paper are to summarize and evaluate the literature on athletes’ increased vulnerability to mental ill-health and digital mental health solutions as a complement to prevention and intervention, and to show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking, and faster and better treatment algorithms. METHODS This mini review shapes literature in the fields of athlete mental health and digital mental health by summarizing and evaluating journal and review articles drawn from PubMed Central and the Directory of Open Access Journals. RESULTS Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence to the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning, and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for use in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing, digital mental health screening, and tracking tools have the potential to drive a consolidated, measurable, and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health. CONCLUSIONS Athletes are an <i>at-risk</i> subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience that helped them to positively adjust after a period of <i>overwhelming</i> stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health, especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely used among general populations, but there is yet to be an eminent athlete version. Sports in combination with mental health education should address the barriers to help-seeking by increasing awareness, from mental ill-health to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies, that may be used in prevention and early intervention strategies.

Mental health screening system using intranet setting in a company.

1999 ◽  
Author(s):  
Minoru Arai ◽  
Daisuke Mori ◽  
Tetsu Kawamura ◽  
Hideo Fumimoto ◽  
Masagi Shimazaki ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Screening ◽  
Mental Health Screening ◽  
Screening System ◽  
A Company

Copenhagen Infant Mental Health Screening

2016 ◽  
Author(s):  
Janni Ammitzbøll ◽  
Bjørn E. Holstein ◽  
Lisbeth Wilms ◽  
Anette Andersen ◽  
Anne Mette Skovgaard
Keyword(s):  
Mental Health ◽  
Health Screening ◽  
Mental Health Screening ◽  
Infant Mental Health

Health professional perspectives on an antenatal mental health screening program in a private hospital

2021 ◽  
Author(s):  
Jane Kohlhoff ◽  
Sara Cibralic ◽  
Sarah Tooke ◽  
Rachael Hickinbotham ◽  
Catherine Knox ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Professional ◽  
Private Hospital ◽  
Screening Program ◽  
Health Screening ◽  
Mental Health Screening ◽  
Professional Perspectives ◽  
Health Screening Program

scholarly journals POS1321 MENTAL HEALTH SCREENING IN PATIENTS WITH JUVENILE IDIOPATHIC ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 943.1-943
Author(s):  
S. Eulert ◽  
M. Niewerth ◽  
J. Hörstermann ◽  
C. Sengler ◽  
D. Windschall ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Mental Health Problems ◽  
Functional Limitations ◽  
Health Problems ◽  
Health Screening ◽  
Mental Health Screening ◽  
Psychological Support ◽  
Web Based ◽  
Patient Health

Background:Mental disorders often begin in the vulnerable phase of adolescence and young adulthood. Young people with chronic diseases are particularly at risk. Early recognition of mental health problems is necessary in order to be able to support those affected in a timely and adequate manner. By implementing a web-based generic screening tool for mental health in routine care, patients with juvenile idiopathic arthritis (JIA) and mental health conditions can be identified and provided with targeted treatment.Objectives:To investigate the prevalence of mental health conditions in young people with JIA in routine rheumatology care.Methods:Mental health screening is implemented as an add-on module to the National Paediatric Rheumatology Database (NPRD). The current data was gathered over a period of 24 months. Patients complete the screening tool which includes the Patient Health Questionnaire1 (PHQ-9, score 0-27) and the Generalized Anxiety Disorder scale2 (GAD-7, score 0-21) via a web-based questionnaire. The cut-off for critical values in PHQ-9 and GAD-7 were defined as values ≥ 10. Simultaneously, other data, such as sociodemographic data, disease activity (cJADAS10, score 0-30), functional status (CHAQ, score 0-3) were collected as well.Results:The analysis included 245 patients (75% female) with a mean age of 15.7 years and a mean disease duration of 8.8 years. 38.8% of the patients had oligoarthritis (18.0% OA, persistent/20.8% OA, extended) and 23.3% RF negative polyarthritis. At the time of documentation 49 patients (30.6%) had an inactive disease (cJADAS10 ≤ 1) and 120 (49.4%) no functional limitations (CHAQ = 0). In total, 53 patients (21.6%) had screening values in either GAD-7 or PHD-9 ≥10. Patients with critical mental health screening values showed higher disease activity and more frequent functional limitations than inconspicuous patients (cJADAS10 (mean ± SD): 9.3 ± 6 vs. 4.9 ± 4.9; CHAQ: 0.66 ± 0.6 vs. 0.21 ± 0.42). When compared to males, females were significantly more likely to report either depression or anxiety symptoms (11.7% vs. 24.9%, p = 0.031).17.6% of all patients with valid items for these data reported to receive psychological support, meaning psychotherapeutic support (14.5%) and/or drug therapy (8.6%). Among those with a critical mental health screening score, 38.7% received psychological support (psychotherapeutic support (35.5%) and/or drug therapy (16.1%)).Conclusion:Every fifth young person with JIA reported mental health problems, however, not even every second of them stated to receive psychological support. The results show that screening for mental health problems during routine adolescent rheumatology care is necessary to provide appropriate and targeted support services to young people with a high burden of illness.References:[1]Löwe B, Unützer J, Callahan CM, Perkins AJ, Kroenke K. Monitoring depression treatment outcomes with the patient health questionnaire-9. Med Care. 2004 Dec;42(12):1194-201.[2]Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 22; 166(10):1092-7.[3]The screening data were collected as part of COACH (Conditions in Adolescents: Implementation and Evaluation of Patient-centred Collaborative Healthcare), a project supported by the Federal Ministry of Education and Research (FKZ: 01GL1740F).Disclosure of Interests:Sascha Eulert: None declared, Martina Niewerth: None declared, Jana Hörstermann: None declared, Claudia Sengler: None declared, Daniel Windschall: None declared, Tilmann Kallinich: None declared, Jürgen Grulich-Henn: None declared, Frank Weller-Heinemann Consultant of: Pfizer, Abbvie, Sobi, Roche, Novartis, Ivan Foeldvari Consultant of: Gilead, Novartis, Pfizer, Hexal, BMS, Sanofi, MEDAC, Sandra Hansmann: None declared, Harald Baumeister: None declared, Reinhard Holl: None declared, Doris Staab: None declared, Kirsten Minden: None declared

scholarly journals Mental health screening in adolescents with CFS/ME

2021 ◽  
Author(s):  
Maria E. Loades ◽  
Paul Stallard ◽  
David Kessler ◽  
Esther Crawley
Keyword(s):  
Mental Health ◽  
Health Screening ◽  
Mental Health Screening
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