Recent Developments in the Treatment of Depression

• Published in: Behavior therapy 2019-03, Vol.50 (2), p.257-269
• Main Authors: Hollon, Steven D., Cohen, Zachary D., Singla, Daisy R., Andrews, Paul W.
• Format: Article
• Language: English
• Subjects: antidepressant medications; Antidepressive Agents - therapeutic use; Behavior Therapy - methods; Behavior Therapy - trends; Chronic Disease; cognitive behavior therapy; Depression - diagnosis; Depression - psychology; Depression - therapy; Humans; iatrogenic effect; Machine Learning - trends; Psychotherapy - methods; Psychotherapy - trends; Stress, Psychological - diagnosis; Stress, Psychological - psychology; Stress, Psychological - therapy; treatment selection algorithms, global mental health
• ISSN: 0005-7894; 1878-1888
• DOI: 10.1016/j.beth.2019.01.002

The cognitive and behavioral interventions can be as efficacious as antidepressant medications and more enduring, but some patients will be more likely to respond to one than the other. Recent work has focused on developing sophisticated selection algorithms using machine-learning approaches that answer the question, “What works best for whom?” Moreover, the vast majority of people suffering from depression reside in low- and middle-income countries where access to either psychotherapy or medications is virtually nonexistent. Great strides have been made in training nonspecialist providers (known as task sharing) to overcome this gap. Finally, recent work growing out of evolutionary psychology suggests that antidepressant medications may suppress symptoms at the expense of prolonging the underlying episode so as to increase the risk of relapse whenever someone tries to stop. We address each of these developments and their cumulative implications. •Selection algorithms can be used to identify optimal treatment for a given patient•Task sharing can make treatment accessible in low- and middle-income countries•Antidepressants may suppress symptoms at the expense of prolonging the episode