Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

ABSTRACT:

Objective

Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making.

Materials and methods

Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depre

SUBMITTER: Figueroa CA 

PROVIDER: S-EPMC8200266 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature