Evidence map›Paper›PMID 37687813›Full record

ArticleSensors (Basel, Switzerland)2023

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health.

Benjamin D Maylor, Charlotte L Edwardson, Alexandra M Clarke-Cornwell, Melanie J Davies, Nathan P Dawkins, David W Dunstan, Kamlesh Khunti, Tom Yates, Alex V Rowlands

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

9 authors at 3 institutions in 2 countries.

Benjamin D MaylorDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.ORCID 0000-0002-4503-0479
Charlotte L EdwardsonDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.
Alexandra M Clarke-CornwellSchool of Health and Society, University of Salford, Greater Manchester M5 4WT, UK.ORCID 0000-0001-9510-7676
Melanie J DaviesDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.ORCID 0000-0002-9987-9371
Nathan P DawkinsDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.ORCID 0000-0002-6374-7908
David W DunstanPhysical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Kamlesh KhuntiDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.
Tom YatesDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.
Alex V RowlandsDiabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK.ORCID 0000-0002-1463-697X
University of Leicester · GBBaker Heart and Diabetes Institute · AUUniversity of Salford · GB

Funding

National Institute for Health Research 16/41/04
6 · The paper itself

Abstract

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.

Indexed as

Cardiovascular DiseasesWristAccelerometryExerciseHumansThighaccelerometryactivPALAxivityGGIRmeasurement

Identifiers

PMID37687813
PMCPMC10489920
OpenAlexW4386098402

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.