Evidence map›Paper›PMID 31808014›Full record

ArticleSports medicine - open2019

Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics.

Alex V Rowlands, Nathan P Dawkins, Ben Maylor, Charlotte L Edwardson, Stuart J Fairclough, Melanie J Davies, Deirdre M Harrington, Kamlesh Khunti, Tom Yates

Open access · goldAbstract read
In one paragraph

Article in Sports medicine - open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
4.4field-weighted citation impact, top 5% 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

45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.

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  11. Beyond the snapshot: harnessing daily physical activity for heart failure characterization.Clinical research in cardiology : official journal of the German Cardiac Society · 2025
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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 2 institutions in 2 countries.

Alex V RowlandsDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK. alex.rowlands@leicester.ac.uk.ORCID http://orcid.org/0000-0002-1463-697X
Nathan P DawkinsDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.
Ben MaylorDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0002-4503-0479
Charlotte L EdwardsonDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0001-6485-9330
Stuart J FaircloughMovement Behaviours, Health, and Wellbeing Research Group, Department of Sport and Physical Activity, Edge Hill University, Ormskirk, UK.ORCID http://orcid.org/0000-0001-8358-1979
Melanie J DaviesDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0002-9987-9371
Deirdre M HarringtonDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0003-0278-6812
Kamlesh KhuntiDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0003-2343-7099
Tom YatesDiabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.ORCID http://orcid.org/0000-0002-5724-5178
University of Leicester · GBEdge Hill University · GB

Funding

Department of Health PHR/13/90/30
6 · The paper itself

Abstract

The lack of consensus on meaningful and interpretable physical activity outcomes from accelerometer data hampers comparison across studies. Cut-point analyses are simple to apply and easy to interpret but can lead to results that are not comparable. We propose that the optimal accelerometer metrics for data analysis are not the same as the optimal metrics for translation. Ideally, analytical metrics are precise continuous variables that cover the intensity spectrum, while translational metrics facilitate meaningful, public-health messages and can be described in terms of activities (e.g. brisk walking) or intensity (e.g. moderate-to-vigorous physical activity). Two analytical metrics that capture the volume and intensity of the 24-h activity profile are average acceleration (volume) and intensity gradient (intensity distribution). These allow investigation of independent, additive and interactive associations of volume and intensity of activity with health; however, they are not immediately interpretable. The MX metrics, the acceleration above which the most active X minutes are accumulated, are translational metrics that can be interpreted in terms of indicative activities. Using a range of MX metrics illustrates the intensity gradient and average acceleration (i.e. 24-h activity profile). The M120, M60, M30, M15 and M5 illustrate the most active accumulated minutes of the day, the M

Identifiers

PMID31808014
PMCPMC6895365
OpenAlexW2993558971

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.