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Updated: Mar 13, 2026

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Standardized Methods for Evaluating Physical and Eating Behaviors: The WEALTH Cross-Sectional Study Protocol.

Grainne Hayes1, Christoph Buck2, Greet Cardon3

  • 1Department of Physical Education and Sport Sciences, Health Research Institute, Physical Activity for Health Research Centre, University of Limerick, Limerick, Ireland, 353 860806576.

JMIR Research Protocols
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

The WEALTH project developed standardized methods using wearable sensors to measure physical and eating behaviors (PB & EB). This research aims to create tools for integrated data collection to advance public health research.

Keywords:
eating behaviorsecological momentary assessmentmachine learningphysical behaviorswearables

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Area of Science:

  • Behavioral Science
  • Biomedical Engineering
  • Public Health

Background:

  • Accurate measurement of physical behaviors (PB) and eating behaviors (EB) is crucial for public health.
  • Standardized methods are needed to identify daily PBs and EBs using wearable sensors.
  • The Wearable Sensor Assessment of Physical and Eating Behaviours (WEALTH) project addresses this need.

Purpose of the Study:

  • To develop standardized methods for identifying daily PBs and EBs from wearable sensors.
  • To evaluate the interaction and contexts of these behaviors.
  • To describe the study design, methods, and participant characteristics.

Main Methods:

  • A cross-sectional study conducted in 5 European centers with 627 participants.
  • Utilized research- and consumer-grade wearable devices for data collection.
  • Employed ecological momentary assessments (EMAs) and 24-hour dietary recalls.

Main Results:

  • Data from 627 participants (44% male, mean age 32.7 years, mean BMI 24.5 kg/m²) were collected.
  • Machine learning (ML) models are being developed for activity classification from accelerometer data.
  • Primary results are anticipated in 2026.

Conclusions:

  • The WEALTH project will provide a repository of labeled data and ML models.
  • A methodology for simultaneously capturing EB and PB will be available.
  • This integrated system will support future research in behavior monitoring.