Implementation of Active Workstations in University Libraries—A Comparison of Portable Pedal Exercise Machines and Standing Desks
Bibliographic record
Abstract
Sedentary behaviors are an important issue worldwide, as prolonged sitting time has been associated with health problems. Recently, active workstations have been developed as a strategy to counteract sedentary behaviors. The present study examined the rationale and perceptions of university students’ and staff following their first use of an active workstation in library settings. Ninety-nine volunteers completed a self-administered questionnaire after using a portable pedal exercise machine (PPEM) or a standing desk (SD). Computer tasks were performed on the SD (p = 0.001) and paperwork tasks on a PPEM (p = 0.037) to a larger extent. Men preferred the SD and women chose the PPEM (p = 0.037). The appreciation of the PPEM was revealed to be higher than for the SD, due to its higher scores for effective, useful, functional, convenient, and comfortable dimensions. Younger participants (<25 years of age) found the active workstation more pleasant to use than older participants, and participants who spent between 4 to 8 h per day in a seated position found active workstations were more effective and convenient than participants sitting fewer than 4 h per day. The results of this study are a preliminary step to better understanding the feasibility and acceptability of active workstations on university campuses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".