Evidence map›Paper›PMID 33672201›Full record

ArticleSensors (Basel, Switzerland)2021

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study.

Julia Wrobel, John Muschelli, Andrew Leroux

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Not just how much, but how it's done: movement activity bout distributions and everyday cognition in older adults with elevated dementia risk.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2026
    Article
  2. Article
  3. Article
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  5. "International journal of environmental research and public health · 2023
    Article
  6. Can genetics guide exercise prescriptions in osteoarthritis?Frontiers in rehabilitation sciences · 2022
    Article
  7. Article
  8. Article
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

3 authors.

Julia WrobelDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.ORCID 0000-0001-6783-1421
John MuschelliDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-6469-1750
Andrew LerouxDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

Funding

EPIDEMIOLOGY AND BIOSTATISTICS OF AGINGT32AG000247 · NIA · JOHNS HOPKINS UNIVERSITY · PI Karen J. Bandeen-Roche · 1996 to 2026
$13.2M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NIA NIH HHS T32 AG000247
6 · The paper itself

Abstract

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals' levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42-78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.

Indexed as

Biological Specimen BanksExerciseQuality of LifeAdultAgedFemaleHumansMaleMiddle AgedUnited KingdomWrist Jointaccelerometersagingcurve registrationfunctional regressionUK Biobank

Identifiers

PMID33672201
PMCPMC7927049

What OpenQuestion holds

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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.