Does Using Multiple Computer Monitors Affect Health and Productivity? A Systematic Review
Bibliographic record
Abstract
Multiple monitor configurations are used in office settings to promote increased productivity by providing more screen space. Our systematic review compiled literature to determine if office workers who use multiple computer monitor configurations have altered health and performance outcomes compared to the use of a single monitor. A secondary purpose was to compare the studies’ monitor configurations to purchasing trends. Finally, we compiled user preference results and methodological information, such as the tasks used and participant placement. Our systematic review was registered on PROSPERO (Gallagher, Cameron, De Carvalho, & Boule, 2018) a-priori and conducted and reported according to the PRISMA statement guidelines (Moher, Liberati, Tetzlaff, Altman, & Group, 2010). Inclusion criteria were any study that assessed participants over the age of 18 years, looked at office work tasks, and assessed the use of either two or more monitors at a time in comparison to single monitor use. The primary outcomes were changes in health and performance-related variables. Secondary outcomes were user preference, the characteristics of the monitor configurations tested in the study, participant placement with respect to those monitors, and tasks used to assess configuration effectiveness. Two team members (KG & MB) independently screened the titles and abstracts to determine studies that potentially met the inclusion criteria. Justification for inclusion/exclusion was recorded on a standardized form. For all included studies, the independent reviewers separately extracted information and performed a risk of bias assessment. Discrepancies were resolved through discussion and if necessary, consultation with a third reviewer (DC). We included eighteen articles in the systematic review. Four studies were conducted in a field setting using workers’ real tasks and fourteen were conducted in a laboratory setting. Performance outcomes generally improved or remained the same with the use of multiple computer monitors versus a single monitor; however, results were shown to be influenced by the task involved. Health-related outcomes were less consistent and have not been investigated enough on multiple monitor configurations and larger displays. Head rotation from neutral is found with multiple monitor use. Muscle activity and discomfort measures need further assessment, especially for larger monitors. Future work should assess health and performance measures together to get a clear picture of the potential benefits and disadvantages of the monitor setup, be cognizant of the tasks and user placements chosen, consider recent purchasing trends when selecting monitors for research studies, and conduct field studies to assess the influence of monitor choice and placement on performance, and health and well-being.
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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 | 0.024 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".