01
Applied research & evaluation
Mixed-methods research that answers a decision, not just a question. Survey design and mediation analysis, computational text analysis of user-generated data, scoping reviews, outcome measurement and logic models.
Researcher · Consultant · Marine veteran
10,000 apps promise help. Most get deleted.
I work at the intersection of human-computer interaction and digital mental health, studying the distance between downloading help and actually using it. Then I help teams close it.
The problem I work on
Most digital health tools
don't fail at download.
They fail at week three.
Adoption is the easy part. The hard part is what happens after, when the friction accumulates, the value stops being obvious, and the person quietly stops opening the app.
I analyzed 170,127 user reviews and surveyed 314 young adults to find out what actually drives that exit. The answer turned out to be more useful than "the app wasn't good enough."
170,127
App reviews analyzed
with topic modeling
with topic modeling
314
Young adults surveyed
on abandonment
on abandonment
4
Peer-reviewed
publications
publications
What I do
Three ways I'm usually brought in.
02
Systems mapping & facilitation
Getting the whole system in one room and making it legible. Systems Resource Mapping, suicide mortality review, learning collaboratives, stakeholder network mapping, and multi-agency technical assistance, work I've delivered for states, counties and federal partners.
03
Digital health product strategy
Engagement and retention advisory for behavioral health products, grounded in an empirically derived model of what pushes users out, rather than generic UX heuristics. Plus AI and data-literacy training for teams that need to read their own evidence.
In development
The MHMA Friction Compass
A user-testing tool for mental health app development teams. It reads a new app or feature, scores it against a taxonomy of friction points and motivators drawn from real user data, and predicts a range of user outcomes, before a single live participant is recruited.
- Two stages: first it assesses the app against the taxonomy, then a model built on real user responses predicts how people are likely to react.
- Separates healthy churn from real churn, the distinction standard engagement metrics cannot make.
- Built for behavioral health specifically, not a horizontal UX engine pointed at a health app.
A finding worth arguing about
People can like your app and quit it anyway.
Small frustrations pile up and push people out on their own, whether or not they say they're happy with the product. So a strong satisfaction score can look reassuring right up until everyone leaves.
Selected publications
Peer-reviewed work.
- 01 Investigating Post-adoption Abandonment of Mental Health Mobile Applications among Young Adults 2025
- 02 Exploring Digital Affordances in Online Mental Health Resources: A Scoping Review of Methodologies and Populations 2024
- 03 Crisis Intercept Mapping for Community-Based Suicide Prevention 2024
- 04 Exploring the Association Between Suicide Prevention PSAs and User Comments on YouTube 2023
About
From the Marine Corps to a research lab.
I served four years in the United States Marine Corps, including a deployment in support of Operation Iraqi Freedom. That's where I first watched systems meant to support people fail the people inside them.
Two decades later I've built a career on the same question in a different form: how do you design services, systems and software that hold up under the conditions people actually live in? The answer has taken me through an MBA, a Ph.D. in Information Science, and federal technical assistance work.