Religion and Depression in Adolescence [Fruehwirth, Iyer, & Zhang (2016) | PDF]
The probability of being depressed increases dramatically during adolescence and is linked to a range of adverse outcomes. Many studies show a correlation between religiosity and mental health, yet the question remains whether the link is causal. The key issue is selection into religiosity. We exploit plausibly random variation in adolescents’ peers to shift religiosity independently of other individual-level unobservables that might affect depression. Using a nationally representative sample of adolescents in the US, we find robust effects of religiosity on depression, that are particularly strong for the most depressed. These effects are not a result of social context. Instead, we find that religiosity buffers against stressors, possibly through improved social and psychological resources. This has implications especially for effective mental health policy.
Artificial Intelligence Hits the Barrier of Meaning [Melanie Mitchell | New York Times]
The challenge of creating humanlike intelligence in machines remains greatly underestimated. Today’s A.I. systems sorely lack the essence of human intelligence: understanding the situations we experience, being able to grasp their meaning. The mathematician and philosopher Gian-Carlo Rota famously asked, “I wonder whether or when A.I. will ever crash the barrier of meaning.” To me, this is still the most important question.
5 Digital Mental Health Categories Spanning the Care Continuum [The Digital Mental Health Project | Craig DeLarge]
- Online portals & social communities
- Mobile apps, sensors, & algorithms
- TelePsych & therapeutic mobile apps
- Virtual/augmented reality
- Care delivery platforms
Organized according to function:
What it Takes to Succeed: Digital Therapeutics & the Health System (A Primer) [Liz Rockett | KP Ventures]
These successful digital therapeutics companies:
1. Understand the needs of the population they are trying to serve, through extensive interviewing and user testing with patients and all of the stakeholders that will influence utilization and purchasing.
2. Team up with people best able to unlock the potential that exists between the technology, clinical need, and path to market.
3. Design to maximize uptake (adoption) and engagement.
4. Build an evidence base, by rigorously testing and understanding their efficacy, ideally with clinical trials and contributions to peer-reviewed literature. They seek FDA approval if their path to market requires it.
5. Open access channels to get into the hands of their target population.
6. Find a reimbursement schema that they can fit into to get paid, and get paid at scale.
A startup’s bold plan for a mood-predicting smartphone app is shadowed by questions over evidence [Kate Sheridan | Statnews]
The company’s app collects information about how people are typing and runs it through a machine learning algorithm to determine which data can predict their emotional state….
The idea is to use that data to establish a “normal” pattern — so it can be compared against someone’s typing habits on any given day. If the habits look off, slower or more agitated than normal, the app can alert a health care provider.
last two links via the Rethink Behavioral Health Innovation newsletter