Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 3, 2024
Date Accepted: Mar 20, 2025
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Free-Living Physical Activity in Youth (FLPAY): Study Protocol and Methods
ABSTRACT
Background:
Wearable activity monitors are increasingly used to characterize physical behavior. Development and validation of these characterization methods requires criterion labelled data typically collected in a lab or simulated free-living environment, which does not generally translate well to free-living due to limited behavior engagement in development that is not representative of free-living.
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 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 in June 2016 and concluded in March 2020. Data collection is complete.
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 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.
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