State-Based Automation for Time-Restricted Eating Adherence

TL;DR

This paper introduces a graph-based system that automates participant management in a time-restricted eating study, improving adherence monitoring and reducing manual effort for researchers.

Contribution

It presents a novel automated system utilizing a participant graph and texting service to streamline adherence tracking in complex medical study protocols.

Findings

Automated participant management reduces manual workload.

Graph-based approach improves adherence monitoring accuracy.

System enables reliable data collection and validation.

Abstract

Developing and enforcing study protocols is a foundational component of medical research. As study complexity for participant interactions increases, translating study protocols to supporting application code becomes challenging. A collaboration exists between the University of Kentucky and Arizona State University to determine the efficacy of time-restricted eating in improving metabolic risk among postmenopausal women. This study utilizes a graph-based approach to monitor and support adherence to a designated schedule, enabling the validation and step-wise audit of participants' statuses to derive dependable conclusions. A texting service, driven by a participant graph, automatically manages interactions and collects data. Participant data is then accessible to the research study team via a website, which enables viewing, management, and exportation. This paper presents a system for automatically managing participants in a time-restricted eating study that eliminates time-consuming interactions with participants.