NHANES Accelerometry and Derivatives

Lily Koffman and John Muschelli

NHANES

National Health and Nutrition Examination Survey

  • assess the health and nutritional status of adults and children in the United States through a combination of interviews, physical examinations, and laboratory tests.
  • Nationally Representative Sample
    • Can make claims about US population
    • Don’t forget survey weights!
  • Continuously in 2-year cycles
  • Lots of Data

NHANES Accelerometry

Waves 2011-2012 and 2013-2014

  • Physical Activity Data (under Examination Data)

  • Actigraph GT3X+, nondominant wrist

https://wwwn.cdc.gov/nchs/data/nhanes/public/2011/manuals/2012-Physicial-Activity-Monitor-Procedures-Manual-508.pdf

NHANES Accelerometry Data Released

Data Released in 2020 (and updated in 2022) summarized at Day/HourMinute

NHANES Summary: MIMS

Monitor-independent movement summary for accelerometer data (John et al. 2019)

New measure!

Map it back to other measures

We had a lot of research on activity counts - Intuition/cutoffs

  • Used BLSA data
    • older population, but still had variability
    • GT3X files had been processed to Activity Counts (AC) (no open source method yet)
    • Used GT3X to create MIMS (MIMSunit R Package)

Created mapping (Karas et al. 2022)

Rewind 5 years

BIRS 2020: Feb 26, 2020

Almost 5 years to today!

BIRS conference: Use of Wearable and Implantable Devices in Health Research

Schedule: https://workshops.birs.ca/events/20w5109/schedule

  • Waiting on Raw Data!

Recorded Videos: https://www.birs.ca/events/2020/5-day-workshops/20w5109/videos

Then COVID Happened

Including a Failed Startup (for John)

And Deep Learning Made Strides

Back from Leave of Absence

Identify individuals from wrist acceleromtery during walking (“fingerprinting”) (Lily Koffman et al. 2023), want to scale to large, population-based study

  • But need delineated walking and raw data

2022: A Number of Things Happen

  • Release of NHANES Raw Data

NHANES Data Part Deux

Raw data Available

  • 80Hz
  • \(>\) 1Tb compressed!
  • Hourly CSVs and a log file

  • Tarballs of all SEQN identifiers (about 7000 per wave)

NHANES Data Part Deux

Still True Today…Mostly

John Staudenmayer from BIRS 2020:

From https://www.birs.ca/events/2020/5-day-workshops/20w5109/videos/watch/202002271147-Staudenmayer.html

But what if we didn’t need to “train” exactly…

Estimating Steps on Raw Data

  • stepcount (May 25, 2021 initial commit): Scott R. Small et al. (2024)
    • estimate step counts from raw wrist-worn data to 10-second level
  • Uses Self-Supervised Learning (unlabeled and scalable)
  • Applied to UK Biobank then refined on OxWalk (S. R. Small et al. 2022)
  • Performance on other datasets and compared to other open-source step counting algorithms?

Compare to Gold Standard

Lily Koffman and Muschelli (2024) estimates steps using 3 gold standard data sets: OxWalk (S. R. Small et al. 2022), MAREA (Khandelwal and Wickström 2017), and Clemson (Mattfeld, Jesch, and Hoover 2017)

  • iPhone video/force sensitive resistors in shoes/ground facing gold standard

Compare to Gold Standard

Lily Koffman and Muschelli (2024) estimates steps from 5 methods on this data:

Takehome: stepcount SSL not bad

  • stepcount (SSL version) good for most data/metrics:

Takehome: stepcount SSL not bad

  • stepcount (SSL version) good for most data/metrics:

Apply to NHANES

L. Koffman, Crainiceanu, and Muschelli (2024) applies those methods to NHANES.

Wildly different estimates!

Apply to NHANES

Mostly similar patterns by age

Apply to NHANES

Fairly high correlation between methods

Apply to NHANES

And more predictive of 5-year mortality (even than AC/MIMS)

Apply to NHANES

Dose response relationship between steps and mortality

Apply to NHANES

Takehome

  • Steps from different algorithms in NHANES correlated and highly predictive of mortality, but very different in absolute value

  • Steps more interpretable for general public than AC or MIMS, but only if we can better define or harmonize what is a “step”

  • Still need more large, open training data!

  • Still need mapping between stepcount steps, Apple/Fitbit steps, “true” steps

Data Available for Use - PhysioNet

The data is available (more derivatives to come):

https://physionet.org/content/minute-level-step-count-nhanes/1.0.0/

https://physionet.org/static/published-projects/minute-level-step-count-nhanes/1.0.0/

Data Available for Use - PhysioNet

Minute level step counts from 5 algorithms:

Also:

Ripe for functional data analysis, steps, and physical activity research!

References

ActiGraph. 2015. ActiLife Software. Pensacola, FL: ActiGraph LLC.
John, Dinesh, Qu Tang, Fahd Albinali, and Stephen Intille. 2019. “An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.” Journal for the Measurement of Physical Behaviour 2 (4): 268–81.
Karas, Marta, John Muschelli, Andrew Leroux, Jacek K Urbanek, Amal A Wanigatunga, Jiawei Bai, Ciprian M Crainiceanu, and Jennifer A Schrack. 2022. “Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study.” JMIR mHealth and uHealth 10 (7): e38077.
Karas, Marta, Marcin Straczkiewicz, William Fadel, Jaroslaw Harezlak, Ciprian M Crainiceanu, and Jacek K Urbanek. 2021. “Adaptive Empirical Pattern Transformation (ADEPT) with Application to Walking Stride Segmentation.” Biostatistics 22 (2): 331–47.
Khandelwal, Siddhartha, and Nicholas Wickström. 2017. “Evaluation of the Performance of Accelerometer-Based Gait Event Detection Algorithms in Different Real-World Scenarios Using the MAREA Gait Database.” Gait & Posture 51 (January): 84–90. https://doi.org/10.1016/j.gaitpost.2016.09.023.
Koffman, L, C Crainiceanu, and J Muschelli. 2024. “Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011-2014.” Medicine & Science in Sports & Exercise. https://doi.org/10.1249/MSS.0000000000003616.
Koffman, Lily, and John Muschelli. 2024. “Evaluating Step Counting Algorithms on Subsecond Wrist-Worn Accelerometry: A Comparison Using Publicly Available Data Sets.” Journal for the Measurement of Physical Behaviour 7 (1).
Koffman, Lily, Yan Zhang, Jaroslaw Harezlak, Ciprian Crainiceanu, and Andrew Leroux. 2023. “Fingerprinting Walking Using Wrist-Worn Accelerometers.” Gait & Posture 103: 92–98.
Mattfeld, Ryan, Elliot Jesch, and Adam Hoover. 2017. “A New Dataset for Evaluating Pedometer Performance.” In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 865–69. Kansas City, MO: IEEE. https://doi.org/10.1109/BIBM.2017.8217769.
Neishabouri, Ali, Joe Nguyen, John Samuelsson, Tyler Guthrie, Matt Biggs, Jeremy Wyatt, Doug Cross, et al. 2022. “Quantification of Acceleration as Activity Counts in ActiGraph Wearable.” Scientific Reports 12 (1): 11958.
Patterson, Matthew R. 2020. “Verisense Toolbox.” GitHub Repository. https://github.com/ShimmerEngineering/Verisense-Toolbox/tree/master/Verisense_step_algorithm; GitHub.
Rowlands, Alex V., Benjamin Maylor, Nathan P. Dawkins, Paddy C. Dempsey, Charlotte L. Edwardson, Artur A. Soczawa-Stronczyk, Mateusz Bocian, Matthew R. Patterson, and Tom Yates. 2022. “Stepping up with GGIR: Validity of Step Cadence Derived from Wrist-Worn Research-Grade Accelerometers Using the Verisense Step Count Algorithm.” Journal of Sports Sciences 40 (19): 2182–90. https://doi.org/10.1080/02640414.2022.2147134.
Small, S R, L von Fritsch, A Doherty, S Khalid, and A Price. 2022. “OxWalk: Wrist and Hip-Based Activity Tracker Dataset for Free-Living Step Detection and Gait Recognition.” University of Oxford.
Small, Scott R, Shing Chan, Rosemary Walmsley, Lennart von Fritsch, Aidan Acquah, Gert Mertes, Benjamin G Feakins, et al. 2024. “Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.” Medicine and Science in Sports and Exercise 56 (10): 1945.
Straczkiewicz, Marcin, Emily J Huang, and Jukka-Pekka Onnela. 2023. “A ‘One-Size-Fits-Most’ Walking Recognition Method for Smartphones, Smartwatches, and Wearable Accelerometers.” NPJ Digital Medicine 6 (1): 29.
Thapa-Chhetry, Binod, Diego Jose Arguello, Dinesh John, and Stephen Intille. 2022. “Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey.” Medicine and Science in Sports and Exercise 54 (11): 1936–46. https://doi.org/10.1249/mss.0000000000002973.