Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 3, 2024
Date Accepted: Mar 20, 2025
Free-Living Physical Activity in Youth (FLPAY): Protocol and Methods for Calibration and Validation of Machine Learning Models for Physical Behavior Characterization
ABSTRACT
Background:
The Free-Living Physical Activity in Youth (FLPAY) study was designed in two parts to establish a criterion dataset for novel method development for identifying periods of transition between activities in youth.
Objective:
The Free-Living Physical Activity in Youth (FLPAY) study was designed in two parts to establish a criterion dataset for novel method development for identifying periods of transition between activities in youth.
Methods:
The FLPAY study utilized criterion measures of behavior (direct observation) and energy expenditure (indirect calorimetry) to label data from research-grade accelerometer-based devices for the purpose of developing and cross-validating models to identify transitions, classify activity type, and estimate energy expenditure in youth ages 6-18 years old. The first part of the study was a simulated free-living protocol in the lab, comprising short (roughly 60-90 s) and long (roughly 4-5 min) bouts of 16 activities that were completed in various orders over the span of two visits. The second part of the study involved an independent sample of participants who agreed to be measured twice (2 hours each time) in free-living environments such as the home and community.
Results:
The FLPAY study was funded from 2016-2020. A no-cost extension was granted for 2021. A few secondary outcomes have been published, but extensive analysis of primary data is ongoing.
Conclusions:
The two-part design of the FLPAY study emphasized collection of naturalistic behaviors and periods of transition between activities in both structured and unstructured environments. This filled an important gap considering the traditional focus on scripted activity routines in structured laboratory environments. This protocol paper details the FLPAY procedures and participants, along with details about criterion datasets, which will be useful in future studies analyzing the wealth of device-based data in diverse ways.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.