MétaCan
Menu
Back to cohort
Record W2767555125 · doi:10.1177/2165079917738264

Fitbit Activity Trackers Interrupt Workplace Sedentary Behavior: A New Application

2017· article· en· W2767555125 on OpenAlexaff
Nicole A. Guitar, Alicia MacDougall, Denise M. Connelly, Emily Knight

Bibliographic record

VenueWorkplace Health & Safety · 2017
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsSittingSedentary behaviorActivity trackerPhysical therapyPhysical activityPsychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

This study investigated whether Fitbit devices can reduce sedentary behavior among employees in the workplace. Participants were asked to wear Fitbits during 8-hour work shifts, 5 days per week, for 8 weeks. They were instructed to stand at least once every 30 minutes throughout the workday. The goal of the study was to determine whether standing once every 30 minutes was a feasible strategy for reducing sedentary workplace behavior. On average, participants completed 36 of 40 workdays using Fitbits. The number of times participants stood during an 8-hour workday averaged 12 stands per day (maximum 16 stands per day). These results indicate that Fitbit technology is effective for recording and tracking interruptions in sitting time; however, to reduce sitting behavior, alternate approaches are required to motivate larger numbers of workers to participate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.368
Teacher spread0.341 · 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 teacher head, not a consensus.

Study designOther design
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

Citations25
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueWorkplace Health & SafetySame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207