The Journal of Mind and Medical Sciences has published new research on Supportiv’s ability to reach and achieve positive mental health outcomes for teens on government-sponsored health plans.

Adolescents from low-income households are more likely to experience emotional distress, while also facing more barriers to access mental health care such as affordability, stigma, and limited access. Supportiv has been offered to this population, as anonymous digital emotional support offers 24/7, real-time, and safely human-moderated care that circumvents these barriers, providing peer empathy, validation and support.

Examining how government plan-sponsored adolescents and young adults engage with Supportiv’s moderated digital emotional support, a retrospective analysis of 1,005 live chat conversations from 663 Medicaid-sponsored users of Supportiv’s moderated digital emotional support chats, aged 13-20, revealed significant reductions in sentiments of sadness, stress, insecurity, loneliness, anger, anxiety, and improved optimism.

The paper highlights that anonymous, real-time moderated digital emotional support (MDES) may provide low-barrier and accessible emotional support for government plan-sponsored adolescents and young adults who might face increased barriers to typical mental health care.

Even a single MDES live chat session (one-on-one and small-group) was associated with improvements in analyzed sentiments:

  • Anger reduced by 47%.
  • Loneliness reduced by 32%.
  • Stress reduced by 31%.
  • Anxiety reduced by 30%.
  • Sadness reduced by 29%.
  • Insecurity reduced by 24%.
  • Optimism increased by 48%.

Teens discussed many topics, but focused on social connection (40%), mental health struggles (27%), loneliness (10%) and stress (9%). Within conversations discussing social connection, relationships were the most common subtopic.

Teen use was most frequent in the early afternoon and evening hours, highlighting the need for alternative care that is accessible 24/7 beyond traditional care hours.

Read the full analysis here.