Computational model for behavior shaping as an adaptive health intervention strategy
Authored by Sahar Ghanipoor Machiani, Vincent Berardi, Ricardo Carretero-Gonzalez, Neil E Klepeis, Arash Jahangiri, John Bellettiere, Melbourne Hovell
Date Published: 2018
DOI: 10.1093/tbm/ibx049
Sponsors:
United States National Institutes of Health (NIH)
Platforms:
MATLAB
Model Documentation:
Other Narrative
Flow charts
Model Code URLs:
Model code not found
Abstract
Adaptive behavioral interventions that automatically adjust in real-time
to participants' changing behavior, environmental contexts, and
individual history are becoming more feasible as the use of real-time
sensing technology expands. This development is expected to improve
shortcomings associated with traditional behavioral interventions, such
as the reliance on imprecise intervention procedures and
limited/short-lived effects. JITAI adaptation strategies often lack a
theoretical foundation. Increasing the theoretical fidelity of a trial
has been shown to increase effectiveness. This research explores the use
of shaping, a well-known process from behavioral theory for engendering
or maintaining a target behavior, as a JITAI adaptation strategy. A
computational model of behavior dynamics and operant conditioning was
modified to incorporate the construct of behavior shaping by adding the
ability to vary, over time, the range of behaviors that were reinforced
when emitted. Digital experiments were performed with this updated model
for a range of parameters in order to identify the behavior shaping
features that optimally generated target behavior. Narrowing the range
of reinforced behaviors continuously in time led to better outcomes
compared with a discrete narrowing of the reinforcement window. Rapid
narrowing followed by more moderate decreases in window size was more
effective in generating target behavior than the inverse scenario. The
computational shaping model represents an effective tool for
investigating JITAI adaptation strategies. Model parameters must now be
translated from the digital domain to real-world experiments so that
model findings can be validated.
Tags
Agent-based model
selection
Consequences
Reinforcement
Behavior shaping
Jitai
Concurrent schedules