Digital therapy apps make clinical-adjacent recommendations through algorithmic processes they can't explain to users.
The pattern is in the substrate. Once you see it, you see it everywhere.
Mental health apps occupy a strange space: clinical enough to influence behavior, not clinical enough to be regulated. Their recommendation algorithms are opaque. Their matching to user state is opaque. Their crisis detection is opaque. This paper scores 12 mental health apps against the void framework.
The void framework gives this a number. It gives every system a number. The number predicts what happens next.
Digital therapy apps make clinical-adjacent recommendations through algorithmic processes they can't explain to users.
Academic title: Mental Health and Digital Therapy Apps as Void Objects: Clinical Opacity, Engagement Architecture, and Documented Harm — A Within-Domain Spearman Convergence
Move the sliders. Watch the system change state. Pe > 1 means drift wins.
The framework scores these systems — ordered by Pe.
The correlation coefficient. The sample size. The p-value. The math doesn't care about the domain.
Paste any text — AI output, ad copy, a policy document. The scorer runs the same algorithm the framework uses.
Three variables. One ratio. Predicts drift across every domain where the conditions co-occur.
Pe = (O × R) / α
Where O is opacity (how hidden the mechanism is), R is reactivity (how strongly the system responds to you), and α is your independence (how free you are to disengage).
When Pe < 1: diffusion dominates. You can navigate freely. The system is coherent.
When Pe > 1: drift dominates. The system pulls you in a direction. Your agency is reduced.
When Pe >> V* (≈ 3): irreversible cascade. D1 → D2 → D3. The system has captured you.
The framework identifies this pattern in every domain where O, R, and α co-occur. It specifies 26 falsification conditions. 0 of 26 have fired.
Full derivation: 10.5281/zenodo.18826457
Part of the Void Framework — 120 papers, 0/26 kill conditions fired, mean ρ = 0.958.