Evidence map›Paper›PMID 36747782›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults.

Jiaqi Guo, Saul B Gelfand, Erin Hennessy, Marah M Aqeel, Heather A Eicher-Miller, Elizabeth A Richards, Luotao Lin, Anindya Bhadra, Edward J Delp

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 1 country.

Jiaqi GuoSchool of Electrical and Computer Engineering, Purdue University West Lafayette, IN, USA.
Saul B GelfandSchool of Electrical and Computer Engineering, Purdue University West Lafayette, IN, USA.
Erin HennessyFriedman School of Nutrition Science and Policy, Tufts University Boston MA, USA.
Marah M AqeelDepartment of Nutrition Science Purdue University West Lafayette, IN, USA.
Heather A Eicher-MillerDepartment of Nutrition Science Purdue University West Lafayette, IN, USA.
Elizabeth A RichardsSchool of Nursing Purdue University West Lafayette, IN, USA.
Luotao LinDepartment of Nutrition Science Purdue University West Lafayette, IN, USA.
Anindya BhadraDepartment of Statistics Purdue University West Lafayette, IN, USA.
Edward J DelpSchool of Electrical and Computer Engineering, Purdue University West Lafayette, IN, USA.
Purdue University West Lafayette · USTufts University · US

Funding

Temporal Dietary and Physical Activity Patterns Related to Health OutcomesR21CA224764 · NCI · PURDUE UNIVERSITY · PI EICHER-MILLER, HEATHER A · 2018 to 2019
$352k
NCI NIH HHS R21 CA224764
6 · The paper itself

Abstract

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Indexed as

DTWkernel k-meansNHANESphysical activity patterntime series clustering

Identifiers

PMID36747782
PMCPMC9901066
OpenAlexW4318015128

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.