Evidence map›Paper›PMID 31083538›Full record

ArticleSensors (Basel, Switzerland)2019

Effects of Frequency Filtering on Intensity and Noise in Accelerometer-Based Physical Activity Measurements.

Jonatan Fridolfsson, Mats Börjesson, Christoph Buck, Örjan Ekblom, Elin Ekblom-Bak, Monica Hunsberger, Lauren Lissner, Daniel Arvidsson

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 70 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Article
  8. Observational
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Jonatan FridolfssonCenter for Health and Performance, Department of Food and Nutrition, and Sport Science, University of Gothenburg, SE-40530 Gothenburg, Sweden. jonatan.fridolfsson@gu.se.ORCID 0000-0002-7003-4025
Mats BörjessonCenter for Health and Performance, Department of Food and Nutrition, and Sport Science, University of Gothenburg, SE-40530 Gothenburg, Sweden. mats.brjesson@telia.com.
Christoph BuckDepartment of Biometry and Data Management, Leibniz Institute for Prevention Research and epidemiology (BIPS), DE-283 59 Bremen, Germany. buck@leibniz-bips.de.ORCID 0000-0003-0261-704X
Örjan EkblomÅstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, SE-114 86 Stockholm, Sweden. orjan.ekblom@gih.se.
Elin Ekblom-BakÅstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, SE-114 86 Stockholm, Sweden. elin.ekblombak@gih.se.
Monica HunsbergerDepartment of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 90 Gothenburg, Sweden. monica.hunsberger@gu.se.
Lauren LissnerDepartment of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 90 Gothenburg, Sweden. lauren.lissner@gu.se.
Daniel ArvidssonCenter for Health and Performance, Department of Food and Nutrition, and Sport Science, University of Gothenburg, SE-40530 Gothenburg, Sweden. daniel.arvidsson@gu.se.
University of Gothenburg · SESwedish School of Sport and Health Sciences · SELeibniz Institute for Prevention Research and Epidemiology - BIPS · DESahlgrenska University Hospital · SE

Funding

Seventh Framework Programme 266044
6 · The paper itself

Abstract

In objective physical activity (PA) measurements, applying wider frequency filters than the most commonly used ActiGraph (AG) filter may be beneficial when processing accelerometry data. However, the vulnerability of wider filters to noise has not been investigated previously. This study explored the effect of wider frequency filters on measurements of PA, sedentary behavior (SED), and capturing of noise. Apart from the standard AG band-pass filter (0.29-1.63 Hz), modified filters with low-pass component cutoffs at 4 Hz, 10 Hz, or removed were analyzed. Calibrations against energy expenditure were performed with lab data from children and adults to generate filter-specific intensity cut-points. Free-living accelerometer data from children and adults were processed using the different filters and intensity cut-points. There was a contribution of acceleration related to PA at frequencies up to 10 Hz. The contribution was more pronounced at moderate and vigorous PA levels, although additional acceleration also occurred at SED. The classification discrepancy between AG and the wider filters was small at SED (1-2%) but very large at the highest intensities (>90%). The present study suggests an optimal low-pass frequency filter with a cutoff at 10 Hz to include all acceleration relevant to PA with minimal effect of noise.

Indexed as

AccelerometryFemaleHumansMaleMotor ActivitySedentary BehaviorActiGraphadultsAxivitycalibrationchildrenI.FamilyLIV-2013

Identifiers

PMID31083538
PMCPMC6539652
OpenAlexW2944143758

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.