Publications by authors named "Tim Metcalfe"
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 PDFMental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps.
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- 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
- 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.