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Cited by 92Chronic Obstructive Pulmonary Disease (COPD) ResearchPhysical Activity and HealthContext-Aware Activity Recognition Systems

Methodology for Using Long-Term Accelerometry Monitoring to Describe Daily Activity Patterns in COPD

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Hecht, Ariel, Ma, Shuyi, Porszasz, Janos, Casaburi, Richard, for the COPD Clinical Research Netw·2009·COPD: Journal of Chronic Obstructive Pulmonary Disease
DOI 10.1080/15412550902755044PMC PMC2862250
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Abstract

We sought to develop procedures for computerized analysis of long-term, high-resolution activity monitoring data that allow accurate assessment of the time course of activity levels suitable for use in chronic obstructive pulmonary disease (COPD) patients. Twenty-two COPD patients utilizing long-term oxygen recruited from 5 sites of the COPD Clinical Research Network wore a triaxial accelerometer (RT3, Stayhealthy, Monrovia, CA) during waking hours over a 14-day period. Computerized algorithms were composed allowing minute-by-minute activity data to be analyzed to determine, for each minute, whether the monitor was being worn. Temporal alignment allowed determination of average time course of activity level, expressed as average vector magnitude units (VMU, the vectorial sum of activity counts in three orthogonal directions) per minute, for each hour of the day. Mid-day activity was quantified as average VMU/minute between 10AM and 4PM for minutes the monitor was worn. Over the 14 day monitoring period, subjects wore the monitor an average of 11.4 +/- 3.0 hours x day(-1). During mid-day hours, subjects wore the monitor 76.3% of the time and generated an average activity level of 112 +/- 55 VMU x min(-1). Increase in precision of activity estimates with longer monitoring periods was demonstrated. This analysis scheme allows a detailed temporal pattern of activity to be defined from triaxial accelerometer recordings and has the potential to facilitate comparisons among subjects and between subject groups. This trial is registered at ClinicalTrials.gov (NCT00325754).

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  • Hecht et al presented an algorithm for minute-by-minute analysis of patients' activity level based on data recorded using a single unit, and the system was tested in 22 patients monitored over 14 days.

    “Hecht et al [115] presented an algorithm for a minute-by-minute analysis of patients' activity level, based on data recorded using a single unit. The system was tested in 22 patients who were monitored over a period of 14 days.”

    From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [115]

  • Hecht et al implemented a simple empirically-developed algorithm to determine if the subject was wearing the device, providing a handle on compliance.

    “The authors also implemented a simple empirically-developed algorithm to determine if the subject was wearing the device thus providing a handle on compliance.”

    From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [115]

  • Subjects tended to increase their activity level during the first few days of monitoring.

    “Another interesting observation from the same study was that subjects tended to increase their activity level during the first few days of monitoring.”

    From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [115]

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Authors
Hecht, A., Ma, S., Porszasz, J., Casaburi, R., & Netw, F. T. C. C. R.
Affiliations
University of California, Los Angeles (US)
ORCID
Venue
COPD: Journal of Chronic Obstructive Pulmonary Disease
Publisher
Informa UK Limited
Published
2009-01
Volume
6
Issue
2
Pages
121-129
ISSN
  • 1541-2555 (ISSN-L)
  • 1541-2563
DOI
10.1080/15412550902755044
PMC
PMC2862250
OpenAlex
W2110357096
Cited by
91
References
39
Citations by year
2026: 1 · 2025: 4 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 6 · 2015: 5 · 2014: 8 · 2013: 5 · 2012: 11
FWCI
7.32
Type
journal article
Language
en
Open access
Locations
  • COPD Journal of Chronic Obstructive Pulmonary Disease (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Brigham and Women's Hospital
  • University of Minnesota
  • University of Pittsburgh
  • Deutsches Krebsforschungszentrum
  • Temple University
  • University of California, San Francisco
  • National Heart, Lung, and Blood Institute (HL074424, U10HL074418, U10HL074407, U10HL074431, HL074422, HL074409, 1U10-HL074424, U10HL074424, HL074439, U10HL074422, 1U10-HL074416, HL 074428, U10HL074416, U10HL074409, HL074418, HL074407, 1U10-HL074431, HL074431, U10HL074439, U10HL074441, HL074408, HL074416, U10HL074428, HL074441, U10HL074408)
  • National Center for Research Resources (RR02635, M01 RR 02635, M01-RR00056, RR-00051, M01RR-00051, RR00425, M01RR000056, M01RR000051, M01RR016500, M01RR002635, RR 16500, RR 00056, M01RR000425, M01RR 16500, M01-RR00425)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
chronic obstructive pulmonary disease, daily activity patterns, triaxial accelerometer, temporal activity patterns
MeSH terms
Acceleration, Activity Cycles, Aged, Algorithms, Decision Trees, Female, Humans, Male, Middle Aged, Motor Activity, Numerical Analysis, Computer-Assisted, Oxygen Inhalation Therapy, Patient Compliance, Reproducibility of Results, Monitoring, Ambulatory, Pulmonary Disease, Chronic Obstructive

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