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Characterizing daily physical activity patterns with unsupervised learning via functional mixture models

Journal of Behavioral Medicine(2024)10.1007/s10865-024-00519-wSource: DataRank Database

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models is a research paper published in Journal of Behavioral Medicine (2024). On theSindex it has a DataRank of 0. It has been cited 5 times.

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0DataRank · unranked

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Open Access5 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 1.17)OA: greenTopics (63)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (7)

Billy A. Caceres ORCID,Kasey Jackman ORCID,Jeff Goldsmith ORCID,Niurka Suero-Tejeda ORCID,Michelle Odlum ORCID

Live enrichment

PubMed:39306631 PMC:PMC12967293 OpenAlex:https://openalex.org/W4402707477

National Institute of Nursing Research

Grant: P30NR016587

National Heart, Lung, and Blood Institute

Grant: K01HL146965

National Heart, Lung, and Blood Institute

Grant: R01AG069041

National Institutes of Health

Grant: 5P30NR016587-02

Precision in Symptom Self-Management (PriSSM) Center

National Institutes of Health

Grant: 5K01HL146965-03

Examining associations of sexual identity, life experiences, and cardiovascular disease risk in sisters

National Institutes of Health

Grant: 1R01AG069041-01

Social Convoys, Cognitive Reserve and Resilience, and Risk for Alzheimer's Disease and Related Dementias

FWCI

1.17

Citation Percentile

0.8%

Citation Trend

2025

2026

greenLicense: Springer Nature TDM

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12967293/pdf/nihms-2138447.pdf

GREENhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12967293/pdf/nihms-2138447.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12967293/pdf/nihms-2138447.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/s10865-024-00519-w.pdf

publisherhttps://link.springer.com/article/10.1007/s10865-024-00519-w/fulltext.html

Fields of Study

Physical Activity and HealthContext-Aware Activity Recognition SystemsHuman Mobility and Location-Based AnalysisMedicineEnvironmental ScienceHumansExerciseFemaleMaleMiddle AgedAdultAccelerometryAgedBayes TheoremSedentary BehaviorYoung AdultCluster AnalysisUnsupervised Machine LearningPhenotype

MeSH Terms

Unsupervised Machine LearningAdultAgedBayes TheoremFemaleHumansMaleMiddle AgedPhenotypeModels, StatisticalExerciseCluster AnalysisYoung AdultSedentary BehaviorAccelerometry

Keywords

Health psychologyPhysical activityPsychologyBehavioral medicineUnsupervised learningArtificial intelligenceComputer scienceCognitive psychologyPublic healthPhysical medicine and rehabilitationMedicineClinical psychologyClusteringFunctional DataAccelerometryMaleAdultBayes TheoremMiddle AgedArticleYoung AdultPhenotypeHumansCluster AnalysisFemaleSedentary BehaviorExerciseAgedUnsupervised Machine Learning