Evidence map›Paper›PMID 38971929›Full record

ArticleCommunications medicine2024

Hourly step recommendations to achieve daily goals for working and older adults.

Gregory Ang, Chuen Seng Tan, Nicole Lim, Jeremy Tan, Falk Müller-Riemenschneider, Alex R Cook, Cynthia Chen

Abstract read
In one paragraph

Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Gregory AngDepartment of Statistics and Data Science, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0001-7809-8237
Chuen Seng TanSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore.
Nicole LimHealth Promotion Board, Ministry of Health, Singapore, Singapore.
Jeremy TanHealth Promotion Board, Ministry of Health, Singapore, Singapore.
Falk Müller-RiemenschneiderSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore.
Alex R CookDepartment of Statistics and Data Science, National University of Singapore, Singapore, Singapore.
Cynthia ChenSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore. ephchc@nus.edu.sg.ORCID http://orcid.org/0000-0002-1947-8069

Funding

Ministry of Health -Singapore (MOH) PHRGOC22Jul-0020_MOH-001315
6 · The paper itself

Abstract

backgroundThe widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

methodsWe used 24-h steps data from the National Steps Challenge

resultsParticipants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

conclusionsWe provide recommendations on the hourly targets to achieve daily goals, based on different participants' characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

Identifiers

PMID38971929
PMCPMC11227519

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.