Effects of a Novel Machine Learning Mobile App on Diet Adherence in Individuals Following the Low Fodmap Diet
Condition: IBS - Irritable Bowel Syndrome
MeSH terms: Irritable Bowel Syndrome
Study type: Interventional

Summary: 
      A high fermented, oligio-, di-, monosaccharide, and polyols (FODMAP) diet has been shown to
      exacerbate the symptoms of irritable bowel syndrome (IBS). Previous literature has shown
      significant improvement in IBS symptoms after adherence to a low FODMAP diet (LFD); hence,
      LFD is a viable treatment method for IBS and IBS-like symptoms. However, adherence to the LFD
      has proven to be difficult with participants stating that information provided by medical
      practitioners is generalized and nonspecific requiring them to search for supplementary
      information to fit their individual needs. Notably, studies that have used a combination of
      online and in-person methods for treatment have shown improved adherence to the LFD. The
      purpose of this study is to determine whether a novel machine learning dietary mobile
      application (ML-App) will improve adherence to the LFD compared to a standard online dietary
      intervention in populations with IBS or IBS-like symptoms over a 4 week period.
    

Detailed description: 
      Subjects will be residents of California or Arizona and recruited to the study via response
      to the study recruitment questionnaire advertised through online flyers, social media
      platforms (i.e., facebook and instagram), list serves, and recruitment services across all
      Arizona State University (ASU) campuses. This 6-week randomized controlled experimental study
      consists of a 4-week intervention period that immediately follows a 2-week baseline symptom
      monitoring period. The trial is conducted completely online. Data analyses will begin
      immediately once the trial is initiated and is expected to occur for up to one year after
      study completion. Following a 2-week monitoring period, participants will be randomized (via
      a number draw randomization method) into one of three groups: Machine Learning (ML) App +
      Registered Dietitian (RD) facilitator (ML-RD), ML mobile application (ML-App), or Standard
      Dietary Education (CON). The ML-RD group will be provided access to the Heali mobile
      application as well as a personal RD to provide nutrition support and answer any questions
      through a real-time messaging system within the mobile app. The ML-APP group receives access
      to the Heali mobile dietary application only. All groups will receive a link to resources
      regarding the LFD including an educational guide to the implementation of the elimination and
      reintroduction phases of the diet, a suitable/unsuitable food guide, 19 substitutes for
      unsuitable foods, a guide of example meals (i.e., breakfast-6 recipes, lunch-8 recipes,
      dinner-12 recipes, and snacks/beverages-13 recipes), and tips for reading labels and eating a
      balanced diet while following the LFD. The website is located at
      http://www.myginutrition.com/news.html and operated by the University of Michigan Health
      System.
    

Intervention name(s): Heali mobile application + Registered Dietitian; Heali mobile application; Standard Dietary Education
Intervention type(s): Other; Other; Other
Intervention description(s): The Heali mobile application is a dietary resource validated by RD's which uses AI and ML to scan menus and barcodes to provide individuals with information regarding nutritive content and applicability to one's diet using an overall food score and traffic light system. Users have the ability to choose a number of therapeutic diets including LFD, SCD, GERD, and Lectin Free to name a few, but for this study, the app will be isolated to only the LFD. RDs employed by Heali provide real-time messaging to users. For this study, participants will be given access via cell phone login and password (names will not be used). Participant usage information will be de-identified and automatically sent to excel for study authors to perform statistical analysis.; The Heali mobile application is a dietary resource validated by RD's which uses AI and ML to scan menus and barcodes to provide individuals with information regarding nutritive content and applicability to one's diet using an overall food score and traffic light system. Users have the ability to choose a number of therapeutic diets including LFD, SCD, GERD, and Lectin Free to name a few, but for this study, the app will be isolated to only the LFD. For this study, participants will be given access via cell phone login and password (names will not be used). Participant usage information will be de-identified and automatically sent to excel for study authors to perform statistical analysis.; An educational guide to the implementation of the elimination and reintroduction phases of the diet, a suitable/unsuitable food guide, 19 substitutes for unsuitable foods, a guide of example meals (i.e., breakfast-6 recipes, lunch-8 recipes, dinner-12 recipes, and snacks/beverages-13 recipes), and tips for reading labels and eating a balanced diet while following the LFD. The website is located at http://www.myginutrition.com/news.html and operated by the University of Michigan Health System.
Allocation: Randomized
Intervention model: Parallel Assignment
Intervention model description: Randomized Controlled Trial
Masking: None (Open Label)
Primary purpose: Treatment
Keywords: high fermented, oligio-, di-, monosaccharide, and polyols; mobile app; irritable bowel syndrome