Reducing the risks when using benzodiazepines to treat insomnia: A public health approach.

Benzodiazepines are widely used but can cause considerable harm, including sedation, addiction, falls, fractures, and cognitive impairment, especially with long-term use and in elderly patients. The authors propose a public health approach to reduce the potential for harm when using benzodiazepines to treat insomnia. Primary prevention involves judicious patient selection and patient education. Secondary prevention requires keeping the duration of use as short as possible according to guidelines. Tertiary prevention, for patients who have been taking a benzodiazepine for a long time, uses shared decision-making to introduce a gradual and carefully monitored taper.

A practical guide for buprenorphine initiation in the primary care setting.

Buprenorphine is a safe and effective treatment for opioid use disorder but remains underutilized because a major challenge of conventional buprenorphine initiation (termed induction) is that the patient must already be in opioid withdrawal. Previous legal barriers and clinician lack of familiarity with the unique pharmacology of buprenorphine have also limited its use. In this review, we outline changes regarding buprenorphine prescribing laws and physician perceptions of buprenorphine. We also review buprenorphine pharmacology and novel low-dose buprenorphine induction procedures that can be adopted in primary care settings to improve treatment acceptability, retention, and outcomes.

Treating ADHD and Comorbid Anxiety in Children: A Guide for Clinical Practice.

Attention-deficit hyperactivity disorder (ADHD) is frequently comorbid with anxiety disorders with rates as high as 25% to 50% in children and adolescents. Despite various treatment options for ADHD symptoms, limited research addresses treatment in the context of comorbidity. This article seeks to provide a review of the evidence regarding treatment of this comorbid population. Distinct emotional, cognitive, and behavioral symptoms have been observed in this population, suggesting a need for tailored treatment. Despite common concerns about anxiety exacerbation, stimulant medications demonstrate good tolerability and good response in addressing symptoms. Atomoxetine has also demonstrated some benefit and good tolerability for treating this comorbid population. Selective serotonin reuptake inhibitors can be used as adjunctive treatment for anxiety but require careful monitoring of side effects. Cognitive behavioral therapy (CBT) is an important treatment to improve anxiety symptoms in the absence of significant ADHD symptoms. Psychosocial interventions are also essential to improve outcomes.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives.

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.