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Record W2075425150 · doi:10.1123/jpah.2014-0451

Using Shared Treadmill Workstations to Promote Less Time Spent in Daily Low Intensity Physical Activities: A Pilot Study

2015· article· en· W2075425150 on OpenAlexaff
Danielle R. B̀ouchard, Shaelyn M. Strachan, Leslie Johnson, Fiona J. Moola, Radhika Chitkara, Diana E. McMillan, Semone B. Myrie, Gordon G. Giesbrecht

Bibliographic record

VenueJournal of Physical Activity and Health · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSittingTreadmillPhysical therapyBlood pressureMedicineIntensity (physics)Physical activityInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Our objective was to test the feasibility of sharing treadmill workstations among office workers to reduce time spent at low intensity and explore changes in health outcomes after a 3-month intervention. METHODS: Twenty-two office workers were asked to walk 2 hours per shift on a shared treadmill workstation for 3 months. Physical activity levels (ie, low, light, moderate, and vigorous), health-related measures (eg, sleep, blood pressure), treadmill usage information, and questions regarding participants' expectation and experiences were collected. RESULTS: Physical activity time at low intensity during workdays was reduced by 20.1% (P = .007) in the 71% of participants completing the study. Participants were 70% confident that they would keep using the treadmill workstations. Interestingly, systolic blood pressure, diastolic blood pressure, and sleep quality scores were significantly improved (P < .05). CONCLUSIONS: The use of such equipment to replace a few hours of sitting is feasible and might offer important health benefits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.248
GPT teacher head0.430
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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