Publications by authors named "Giles Barton-Owen"
Background: Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders.
View Article and Find Full Text PDFImportance: Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools.
Objective: To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information.
Design, Setting, And Participants: This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020.
Digital mental health interventions (DMHI) have the potential to address barriers to face-to-face mental healthcare. In particular, digital mental health assessments offer the opportunity to increase access, reduce strain on services, and improve identification. Despite the potential of DMHIs there remains a high drop-out rate.
View Article and Find Full Text PDFBackground: Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with this condition.
Objective: This study aims to provide evidence for an extended definition of MDD symptomatology.
Methods: Symptom data were collected via a digital assessment developed for a delta study.
Article Synopsis
- SSRIs are commonly used to treat depression, but their effectiveness can differ among patients, prompting a study to identify factors that predict how well someone may respond to these medications.
- Using data from an online mental health questionnaire and advanced analysis techniques, researchers found that positive affectivity was the strongest predictor of SSRI response, while chronic pain, sleep problems, and unemployment negatively impacted treatment perception.
- The study highlighted the need for caution in interpreting results due to its exploratory nature, reliance on self-reported data, and the necessity for further research to confirm these findings.
Article Synopsis
- - The Delta Study aimed to enhance the diagnosis of mood disorders in people with low mood, seeking to estimate their prevalence and characteristics, and how these findings can influence clinical practices.
- - Participants were classified into three groups based on their mood disorder history, and comprehensive mental health data was gathered online using standardized assessments to establish accurate diagnoses.
- - Findings revealed significant under- and misdiagnosis rates, with notable percentages of Bipolar Disorder (BD) and Major Depressive Disorder (MDD) among participants; this highlights the necessity for better mental health screening in primary care to prevent worsening symptoms.
Article Synopsis
- Web-based mental health assessments can provide earlier and more cost-effective diagnoses for psychiatric conditions than traditional methods, particularly for those showing symptoms of depression.
- A study with over 2000 participants assessed the impact of a web-based assessment that offered personalized feedback and psychoeducation, leading to positive self-reported outcomes in mental well-being after 6 and 12 months.
- While a majority found the web assessment useful for understanding their mental health, a small percentage actually discussed their results with professionals, resulting in limited new diagnoses despite the assessment's predictive accuracy.
Article Synopsis
- Mood disorders often suffer from under- and misdiagnosis, which leads to ineffective treatment and poor outcomes; this study aimed to create a diagnostic algorithm to differentiate bipolar disorder (BD) from major depressive disorder (MDD).
- Researchers recruited individuals aged 18-45 with depressive symptoms online, using a mental health questionnaire and blood samples for biomarker analysis, alongside established diagnostic interviews.
- The developed algorithm showed a high accuracy in distinguishing BD from MDD with an AUROC of 0.92, and further validation confirmed its effectiveness across different patient groups, potentially improving timely diagnosis and treatment for BD.
Article Synopsis
- * This study aimed to create diagnostic models using data from blood samples and a digital mental health assessment to distinguish individuals with MDD from those with low mood.
- * The models showed strong predictive performance, identifying key blood proteins and mental health indicators, suggesting they could help with earlier and more accurate MDD diagnoses in clinical practice.
Article Synopsis
- - Mood disorders impact millions globally and current diagnostic methods often lead to delays in accurate diagnosis, highlighting the need for improved approaches that can facilitate early identification of these conditions.
- - The Delta Trial aims to create an algorithm that combines symptom data with proteomic biomarkers to enhance diagnostic accuracy, particularly to differentiate between bipolar disorder and major depressive disorder.
- - Over 3200 people participated in the Delta Trial, with hundreds providing necessary blood samples and completing follow-up questionnaires, which supports the trial's potential in developing more effective diagnostic methods.