Effects of a Novel AI Mobile App on Symptom Severity in Subjects with Irritable Bowel Syndrome: A Randomized Controlled Trial
Year: 2020
Type: dissertation
Abstract: : Introduction: A diet high in fermented, oligio-, di-, monosaccharide, and polyols\n\n(FODMAP) has been shown to exacerbate symptoms of irritable bowel syndrome\n\n(IBS). Previous literature has shown significant improvement in IBS symptoms after\n\nadherence to a low FODMAP diet (LFD). However, dietary adherence to the LFD is\n\ndifficult with patients stating that information provided by healthcare providers\n\n(HCPs) is generalized and nonspecific requiring them to search for supplementary\n\ninformation to fit their needs. Notably, studies that have used a combination of\n\nonline and in-person methods for treatment have shown improved adherence to the\n\nLFD. Objective: To determine whether a novel artificial intelligence (AI) dietary\n\nmobile application will improve adherence to the LFD compared to a standard online\n\ndietary intervention (CON) in populations with IBS or IBS-like symptoms over a 4-\n\nweek period. Methods: Participants were randomized into two groups: APP or CON.\n\nThe intervention group was provided access to an AI mobile application, a dietary\n\nresource verified by registered dietitians which uses artificial intelligence to\n\nindividualize dietary guidance in real-time with the ability to scan menus and\n\nbarcodes and provide individuals with food scores based on their dietary preferences.\n\nPrimary measures included mobile app engagement, dietary adherence, and\n\nmanifestation of IBS-like symptoms. Baseline Results: A total of 58 participants\n\nwere randomized to groups. This is an ongoing study and this thesis details the\n\nmethodology and baseline characteristics of the participants at baseline and\n\nintervention start. Validation of the application could improve the range of offerings\n\nfor lifestyle diseases treatable through dietary modification.
Author: Aaron Rafferty
Language: English
Subfield: Gastroenterology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3033055288