Proprioception of Cervical Spine Due to Dual Screen Usage Among Office Workers: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Cervical proprioception, assessed using a tracking laser, is often compromised among office workers who use dual screens for extended hours. Prolonged dual-screen usage exceeding six hours daily is associated with proprioceptive deficits. Objective: To investigate the impact of dual-screen usage on cervical spine proprioception among office workers. Methodology: This descriptive cross-sectional study was conducted over a period of six months, from June 2023 to December 2023, following the approval of the research topic. The data collection was carried out at three major organizations: Systems Limited, NETSOL Technologies Ltd., and People I. These institutions were selected to ensure the inclusion of office workers with dual-screen usage, aligning with the study’s objective. A total of 100 participants were included in the study, with the sample size determined by a census method. The sampling technique employed was non-probability purposive sampling. Participants were selected based on specific inclusion criteria, which required them to be between the ages of 24 and 45, of either gender, using dual screens for at least six hours daily over the past two years. Exclusion criteria included individuals with traumatic neck injuries, inflammatory joint diseases, vestibular disorders, recent trauma or surgery, psychiatric disorders, or any neurological or musculoskeletal conditions affecting the neck, shoulder, or back. The study adhered to ethical guidelines approved by the ethical board of Lahore College of Physical Therapy. Categorical data were presented as frequencies and percentages, while continuous variables were expressed as means and standard deviations. Results: The findings revealed greater joint position error during right rotation (5.16±1.91) and left rotation (4.64±1.41) compared to flexion (3.39±1.14) and extension (3.7±1.26), indicating significant proprioceptive deficits in rotational movements. Conclusion: The study concludes that prolonged dual-screen usage among office workers leads to notable cervical proprioceptive deficits, accompanied by continuous muscle strain and poor posture, underscoring the need for ergonomic interventions.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".