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On each day of the closed-loop intervention, denoted by k, mixed-integer quadratic programming is used to make personalized intervention decisions by solving the optimization problem in equation 2 subject to the logical and categorical constraints presented in equation 3 and the following input and output constraints:
ymin – Ψk + j ≤ yk + j ≤ ymax + Ψk + j, j=1,...p
umin ≤ uk + i ≤ umax, i=0,1,...m-1
Δumin ≤ Δuk + i ≤ Δumax, i=0,1,...m-1
Ψk + J ≥ 0, j=1,...p
Variations in the control strategies are implemented
JMIR Res Protoc 2025;14:e70599
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These 3 phases were performed by one author (Dvd S) and checked by another author (AMW-J), both experienced in qualitative analysis. In the fourth phase “reducing data,” the relevant responses were summarized into short codes and sorted into themes, which were thoroughly discussed between 2 authors (Dvd S and AMW-J) up to consensus. Next, we reflected on the data to infer its meaning and implications to refine the themes and determine thematic saturation of the found themes (phase 5, inferring).
J Med Internet Res 2025;27:e71915
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