Evidence map›Paper›PMID 23439414›Full record

ArticleMedicine and science in sports and exercise2013

Walking objectively measured: classifying accelerometer data with GPS and travel diaries.

Bumjoon Kang, Anne V Moudon, Philip M Hurvitz, Lucas Reichley, Brian E Saelens

Abstract read
In one paragraph

Article in Medicine and science in sports and exercise, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

44 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  14. Beyond the bus stop: where transit users walk.Journal of transport & health · 2019
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  19. The Association Between Park Facilities and Duration of Physical Activity During Active Park Visits.Journal of urban health : bulletin of the New York Academy of Medicine · 2018
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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

5 authors.

Bumjoon KangUrban Form Lab and the Department of Urban Design and Planning, University of Washington, Seattle, WA, USA.
Anne V Moudon
Philip M Hurvitz
Lucas Reichley
Brian E Saelens

Funding

The effect of light rail transit on physical activity: a natural experimentR01HL091881 · NHLBI · SEATTLE CHILDREN'S HOSPITAL · PI SAELENS, BRIAN E · 2008 to 2012
$3.2M
NHLBI NIH HHS R01 HL091881PHS HHS NIH/NHLBI R01HL091881
6 · The paper itself

Abstract

purposeThis study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions.

methodsParticipants wore an accelerometer and a GPS unit and concurrently completed a travel diary for seven consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or nonwalking based on a decision-tree algorithm consisting of seven classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts.

resultsThe 706 participants' (mean age = 51 yr, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified PA into 8170 walking bouts (58.5 %) and 5337 nonwalking bouts (38.2%); 464 bouts (3.3%) were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the nonwalking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean ± SD duration of PA bouts classified as walking was 15.2 ± 12.9 min. On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day.

conclusionsGPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or nonwalking behavior.

Indexed as

AlgorithmsGeographic Information SystemsAccelerometryAdultDecision TreesFemaleHumansMaleMiddle AgedRecordsWalking

Identifiers

PMID23439414
PMCPMC3674121

What OpenQuestion holds

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Registered trials

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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.