scholarly journals Cautions, concerns, and future directions for using machine learning in relation to mental health problems and clinical and forensic risks: A brief comment on “Model complexity improves the prediction of nonsuicidal self-injury” (Fox et al., 2019).

2020 ◽  
Vol 88 (4) ◽  
pp. 384-387 ◽  
Author(s):  
Andy P. Siddaway ◽  
Leah Quinlivan ◽  
Nav Kapur ◽  
Rory C. O'Connor ◽  
Derek de Beurs
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Health Problems ◽  
Health Problems ◽  
Model Complexity ◽  
Future Directions ◽  
Self Injury ◽  
Nonsuicidal Self Injury

scholarly journals “Smartphone Apps Are Cool, but Do They Help Me?”: A Qualitative Interview Study of Adolescents’ Perspectives on Using Smartphone Interventions to Manage Nonsuicidal Self-Injury

2021 ◽  
Vol 18 (6) ◽  
pp. 3289
Author(s):  
Anja Čuš ◽  
Julian Edbrooke-Childs ◽  
Susanne Ohmann ◽  
Paul L. Plener ◽  
Türkan Akkaya-Kalayci
Keyword(s):  
Mental Health ◽  
Young People ◽  
Mental Health Problems ◽  
Health Interventions ◽  
Structured Interviews ◽  
Related Factors ◽  
Self Injury ◽  
Qualitative Interview Study ◽  
Mental Health Interventions ◽  
Nonsuicidal Self Injury

Nonsuicidal self-injury (NSSI) is a major mental health problem associated with negative psychosocial outcomes and it most often starts in early adolescence. Despite this, adolescents are rarely involved in informing the development of interventions designed to address their mental health problems. This study aimed to (1) assess adolescents’ needs and preferences about future interventions that are delivered through smartphones and (2) develop a framework with implications for designing engaging digital mental health interventions. Fifteen adolescent girls, aged 12–18 years, who met diagnostic criteria for a current NSSI disorder and were in contact with mental health services, participated in semi-structured interviews. Following a reflexive thematic analysis approach, this study identified two main themes: (1) Experiences of NSSI (depicts the needs of young people related to their everyday experiences of managing NSSI) and (2) App in Context (portrays preferences of young people about smartphone interventions and reflects adolescents’ views on how technology itself can improve or hinder engaging with these interventions). Adolescent patients expressed interest in using smartphone mental health interventions if they recognize them as helpful, relevant for their life situation and easy to use. The developed framework suggests that digital mental health interventions are embedded in three contexts (i.e., person using the intervention, mental health condition, and technology-related factors) which together need to inform the development of engaging digital resources. To achieve this, the cooperation among people with lived experience, mental health experts, and human computer interaction professionals is vital.

scholarly journals Transdiagnostic approaches to mental health problems: Current status and future directions.

2020 ◽  
Vol 88 (3) ◽  
pp. 179-195 ◽  
Author(s):  
Tim Dalgleish ◽  
Melissa Black ◽  
David Johnston ◽  
Anna Bevan
Keyword(s):  
Mental Health ◽  
Mental Health Problems ◽  
Health Problems ◽  
Current Status ◽  
Future Directions

scholarly journals Combining International Survey Datasets to Identify Indicators of Stress during the COVID-19 Pandemic: A Machine Learning Approach to Improve Generalization

COVID ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 728-738
Author(s):  
Eric Yunan Zhao ◽  
Daniel Xia ◽  
Mark Greenhalgh ◽  
Elena Colicino ◽  
Merylin Monaro ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Psychological Stress ◽  
Mental Health Problems ◽  
Health Problems ◽  
International Studies ◽  
Long Term Effects ◽  
Applied Machine Learning ◽  
Drastic Increase ◽  
And Gender

The scale and duration of the worldwide SARS-COVID-2 virus-related quarantine measures presented the global scientific community with a unique opportunity to study the accompanying psychological stress. Since March 2020, numerous publications have reported similar findings from diverse international studies on psychological stress, depression, and anxiety, which have increased during this pandemic. However, there remains a gap in interpreting the results from one country to another despite the global rise in mental health problems. The objective of our study was to identify global indicators of pandemic-related stress that traverse geographic and cultural boundaries. We amalgamated data from two independent global surveys across twelve countries and spanning four continents collected during the first wave of the mandated public health measures aimed at mitigating COVID-19. We applied machine learning (ML) modelling to these data, and the results revealed a significant positive correlation between PSS-10 scores and gender, relationship status, and groups. Confinement, fear of contagion, social isolation, financial hardship, etc., may be some reasons reported being the cause of the drastic increase in mental health problems worldwide. The decline of the typical protective factors (e.g., sleep, exercise, meditation) may have amplified existing vulnerabilities/co-morbidities (e.g., psychiatric history, age, gender). Our results further show that ML is an apropos tool to elucidate the underlying predictive factors in large, complex, heterogeneous datasets without invalidating the model assumptions. We believe our model provides clinicians, researchers, and decision-makers with evidence to investigate the moderators and mediators of stress and introduce novel interventions to mitigate the long-term effects of the COVID-19 pandemic.

Sign in / Sign up

Export Citation Format

Share Document