Figure 2
Example of discrete-time mHealth data and fitted PH-HMM. Probabilities of being in the active state, screen-on counts, mean acceleration magnitude, and hour-of-day random intercepts are plotted against time (hours). Regression models: ,  were fitted for individual i and MAP estimates were calculated using final E-step probabilities . Random intercepts capture the diurnal rhythm of active–rest cycles, with active states mainly occurring between the hours of 6 am–10 pm. Large values of  correspond with a high magnitude in the cyclic diurnal effects. This figure appears in color in the electronic version of this paper, and any mention of color refers to that version.

Example of discrete-time mHealth data and fitted PH-HMM. Probabilities of being in the active state, screen-on counts, mean acceleration magnitude, and hour-of-day random intercepts are plotted against time (hours). Regression models: formula, formula were fitted for individual i and MAP estimates were calculated using final E-step probabilities formula. Random intercepts capture the diurnal rhythm of active–rest cycles, with active states mainly occurring between the hours of 6 am–10 pm. Large values of formula correspond with a high magnitude in the cyclic diurnal effects. This figure appears in color in the electronic version of this paper, and any mention of color refers to that version.

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