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Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

The probability of transitioning into each hidden state at time t is dependent on the previous hidden state, and is given by: Where the elements of A are each of the transition probabilities such that Amn ≡ p(ztn = 1|zt-1,m = 1, ct) denotes the probability of transitioning from state m to state n at time t and we make it explicit that this can depend on a vector of time-varying covariates ct. In addition, other measures can be calculated from the hidden state sequence itself.

Imogen E Leaning, Andrea Costanzo, Raj Jagesar, Lianne M Reus, Pieter Jelle Visser, Martien J H Kas, Christian F Beckmann, Henricus G Ruhé, Andre F Marquand

J Med Internet Res 2025;27:e64007