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Case Study

Obesity management program for adults in primary care using Logmeal Nutraexpert

This program, developed in collaboration with CSA and a CAP (Primary Care Center), aimed to enhance patient adherence and treatment outcomes by incorporating digital food monitoring into a comprehensive obesity management protocol.
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Background & Objectives

Obesity is a growing public health concern and a significant risk factor for chronic diseases, including type 2 diabetes, cardiovascular conditions, and certain types of cancer. Primary care centers (CAPs) are often the first line of prevention and treatment.

This program, developed in collaboration with CSA and a CAP (Primary Care Center), aimed to enhance patient adherence and treatment outcomes by incorporating digital food monitoring into a comprehensive obesity management protocol.

 

Program objectives

  • Early identification and diagnosis of overweight and obesity conditions
  • Reduction of cardiometabolic risk factors
  • Implementation of a multidisciplinary care approach, combining medical, nutritional, and psychological support
  • Integration of AI-based dietary tracking to improve data quality and patient follow-up

 

Initial Assessment

  • Clinical evaluation and laboratory testing of enrolled patients
  • Lifestyle and dietary habit assessments
  • Onboarding patients to the LogMeal APP for digital food logging and real-time dietary monitoring

Digital Monitoring with LogMeal

  • Patients were instructed to log meals daily using their smartphone cameras
  • LogMeal AI automatically analyzed:
  • Calories, macro, and micronutrients (37 indicators)
  • Food groups, ingredients, and portion sizes
  • Enabled tracking of real intake and food waste
  • Weekly review of dietary data by healthcare professionals to adjust goals and interventions accordingly

 

Group and Personalized Interventions

  • Monthly group sessions for nutritional education and lifestyle behavior change
  • Psychological support to address emotional and behavioral components of eating
  • Continuous personalization of nutritional goals based on patient progress and data insights
  • Integration of nutritional data into clinical evaluations for more precise follow-up

Results and value of Logmeal in the program

Outcomes

  • Improved patient engagement and adherence through an easy-to-use, visual food logging system.
  • Collection of accurate, objective, and continuous data on patient food intake.
  • Real-time detection of dietary imbalances and nutritional risks.
  • Better adherence to personalized dietary plans.
  • Positive impact on weight reduction and improvement of metabolic health markers.

 

LogMeal Technology Value in the Program

  • AI-powered food recognition and portion size estimation from images.
  • Comprehensive nutritional analysis covering 37 micro and macro nutrients.
  • Automated food diary creation, eliminating manual input and reducing errors.
  • Monitoring of real intake and food waste for better dietary insights.
  • Professional platform for healthcare teams to monitor progress, adjust goals, and provide tailored feedback.
  • Full integration capability with eHealth applications and patient medical records.

Conclusion

The program is enhancing the ability of healthcare professionals to deliver personalized obesity care using digital tools, while empowering patients to improve their eating habits and lifestyle with real-time feedback and guidance. All participants saved time, automated data entry, minimized errors, and enhanced the quality of nutritional data.

partners and collaborators

HEALTH & NUTRITION REFERRALS

Marta Banque Navarro Dr. PhD
Innovation • CSA – Consorci Sanitari de l’Anoia
MBanque@csa.cat

Nuria Duran Martí
Dietitian-Nutritionist CSA
nduran@csa.cat

The program is enhancing the ability of healthcare professionals to deliver personalized obesity care using digital tools, while empowering patients to improve their eating habits and lifestyle with real-time feedback and guidance. All participants saved time, automated data entry, minimized errors, and enhanced the quality of nutritional data.