Usability of an Intelligent Sit-Stand Desk in Office Teleworkers
Bibliographic record
Abstract
Office work often results in long bouts of time spent sitting without moving, accumulating prolonged static posture (PSP), which might cause musculoskeletal discomfort (MSD). Although sit-stand desks (SSD) allow posture changes, employees do not use them sustainably. In order to automate posture adjustments, an intelligent SSD with an interactive system (iSSD) was created. This study assessed the impact of the iSSD on postural hygiene and explored the user experience. Ten office employees working remotely from home (teleworkers) used the iSSD with (phase B) and without (phase A2) automation. The usage data of the iSSD was measured daily by sensors. We assessed MSDs and working conditions through questionnaires. Semi-structured interviews evaluated participants’ satisfaction. Results showed a 29% decrease in sitting time and absent PSP for phase B. Subsequently, in phase A2, the sitting time returned close to baseline values. Questionnaires reported MSD alleviation and stability of working conditions. Interviews confirmed automation’s benefits for maintaining postural hygiene. Findings suggest that an interactive system can facilitate SSD adoption and promote postural hygiene at the office. HIGHLIGHTSWe added an interactive system to a usual sit-stand desk to force posture change.We tested iSSD usability by teleworkers through data tracked by the system sensors.We conducted qualitative interviews to assess participants’ satisfaction with iSSD.Using an iSSD could help office workers reduce prolonged static postures.Participants appreciated automated posture changes imposed by the interactive system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".