Grant Number:	5R01CA252966-04 Interpret this number
Primary Investigator:	Intille, Stephen
Organization:	Northeastern University
Project Title:	Accelerating the Development of Novel Methods to Measure 24-Hr Physical Behavior
Fiscal Year:	2023

Project Summary/Abstract Accurate measurement of human behavior using devices could significantly advance current knowledge on the dose-response relationships between chronic diseases and behaviors such as physical activity, sedentary behavior, and sleep. The primary objective of this proposal is to develop valid approaches to measure 24-hour physical behavior, as well as to demonstrate a procedure via which those approaches can be compared to others. We aim to help the research community to converge on methods that use devices to accurately measure physical activity type and intensity, sedentary behavior and posture, and sleep in adults. Many promising methods have been proposed to measure behavior from activity monitors. Unfortunately, these methods – which are now being proposed in large numbers – are typically validated on small amounts of data. Thus, they may perform well on lab data, but fail when used in the field on large-scale epidemiological or intervention studies. Moreover, the performance of different methods is rarely compared head-to-head, creating uncertainty for public health researchers about which are the best to use. Quantifying the relative performance of methods that produce similar outcome measures but use different devices or on-body device locations is even more unusual. We will make it easy for researchers interested in physical activity measurement to meaningfully compare performance between new methods and confidently apply those methods to both large-scale surveillance studies and longitudinal interventions. The project has four specific aims: (1) Collect well-annotated data of physical activity, sedentary behavior, and sleep, (2) Use the data from Aim 1 to develop and validate approaches that yield 24-hour estimates of free-living physical activity (type, intensity), sedentary behavior (type, posture), and sleep (wake/sleep, stages), (3) Develop and incrementally refine a suite of tools that researchers can use to easily deploy advanced approaches to measure physical activity, sedentary behavior, and sleep, even for large data, and (4) Use the data and new approaches (Aims 1 and 3) to host four competitions evaluating models, where all entries submitted by other researchers, will be directly compared, ranked, and improved. The goal is to help researchers converge on “gold standard” methods to robustly measure physical activity using common monitor configurations, as well as those devices and configurations likely to be used soon.




Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.
Authors: Tran H. , Potter V. , Mazzucchelli U. , John D. , Intille S. .
Source: Annotation Of Real-world Data For Artificial Intelligence Systems : 9th International Workshop, Arduous 2025, Bologna Italy, October 25-26, 2025, Proceedings. International Workshop On Annotation Of Real-world Data For Artificial Intell.., 2026; 2706, p. 94-118.
EPub date: 2025-10-24 00:00:00.0.
PMID: 41536362
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A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty.
Authors: LE H.A. , Potter V. , Choube A. , Lakshminarayanan R. , Mishra V. , Intille S. .
Source: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies, 2025 Dec; 9(4), .
EPub date: 2025-12-02 00:00:00.0.
PMID: 41488433
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The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.
Authors: Potter V. , Tran H. , Mobley D. , Bertisch S.M. , John D. , Intille S. .
Source: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies, 2025 Dec; 9(4), .
EPub date: 2025-12-02 00:00:00.0.
PMID: 41685089
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An Evaluation of Temporal and Categorical Uncertainty on Timelines: A Case Study in Human Activity Recall Visualizations.
Authors: Potter V. , Le H. , Syeda U.H. , Intille S. , Borkin M.A. .
Source: Visualization : Proceedings Of The ... Ieee Conference On Visualization. Ieee Conference On Visualization, 2025 Nov; 2025, p. 211-215.
PMID: 41502877
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Feasibility and Utility of Multimodal Micro Ecological Momentary Assessment on a Smartwatch.
Authors: Le H. , Potter V. , Lakshminarayanan R. , Mishra V. , Intille S. .
Source: Proceedings Of The Sigchi Conference On Human Factors In Computing Systems. Chi Conference, 2025 Apr-May; 2025, .
EPub date: 2025-04-25 00:00:00.0.
PMID: 41431709
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A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations.
Authors: Le H. , Choube A. , Swain V.D. , Mishra V. , Intille S. .
Source: Proceedings Of The ... Acm International Conference On Ubiquitous Computing . Ubicomp (conference), 2025; 2025(Companion), p. 877-884.
EPub date: 2025-12-29 00:00:00.0.
PMID: 41551017
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Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment.
Authors: LE H.A. , Lakshminarayanan R. , Li J. , Mishra V. , Intille S. .
Source: Proceedings Of The Acm On Interactive, Mobile, Wearable And Ubiquitous Technologies, 2024 Sep; 8(3), .
EPub date: 2024-09-09 00:00:00.0.
PMID: 41180963
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Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey.
Authors: Thapa-Chhetry B. , Arguello D.J. , John D. , Intille S. .
Source: Medicine And Science In Sports And Exercise, 2022-11-01 00:00:00.0; 54(11), p. 1936-1946.
EPub date: 2022-06-23 00:00:00.0.
PMID: 36007161
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