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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

Sex Differences in the Variability of Physical Activity Measurements Across Multiple Timescales Recorded by a Wearable Device: Observational Retrospective Cohort Study

Sex Differences in the Variability of Physical Activity Measurements Across Multiple Timescales Recorded by a Wearable Device: Observational Retrospective Cohort Study

Longitudinal plot of a representative 3-week interval of minute-level metabolic equivalent of task (MET) data (left) from (A) 1 female individual (F, blue) and (B) 1 male individual (M, red), with the histogram of the MET values for each separated by awake (light) and asleep (dark) values (right). MET values were examined at minute-level resolution.

Kristin J Varner, Lauryn Keeler Bruce, Severine Soltani, Wendy Hartogensis, Stephan Dilchert, Frederick M Hecht, Anoushka Chowdhary, Leena Pandya, Subhasis Dasgupta, Ilkay Altintas, Amarnath Gupta, Ashley E Mason, Benjamin L Smarr

J Med Internet Res 2025;27:e66231