State-Based Automation for Time-Restricted Eating Adherence
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909232328081408 |
|---|---|
| author | Armstrong, Samuel E. Mullen, Aaron D. Thomas, J. Matthew Sears, Dorothy D. Pendergast, Julie S. Talbert, Jeffrey Bumgardner, Cody |
| author_facet | Armstrong, Samuel E. Mullen, Aaron D. Thomas, J. Matthew Sears, Dorothy D. Pendergast, Julie S. Talbert, Jeffrey Bumgardner, Cody |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | State-Based Automation for Time-Restricted Eating Adherence Armstrong, Samuel E. Mullen, Aaron D. Thomas, J. Matthew Sears, Dorothy D. Pendergast, Julie S. Talbert, Jeffrey Bumgardner, Cody Human-Computer Interaction Systems and Control 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. |
| title | State-Based Automation for Time-Restricted Eating Adherence |
| topic | Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2406.18718 |