Age-related differences in passive trunk stiffness, seated spine postures, and muscle recruitment patterns
Bibliographic record
Abstract
Introduction: Age is associated with changes in tissue stiffness as well as higher muscle activity and an increase in the prevalence of low back pain. With increased worker age globally, and with the increased incidence of occupational sitting, it is important to understand how trunk stiffness changes with age and how this trunk stiffness is affected by sitting in older, sedentary workers. Methods: 37 women (aged 20-65 years) participated in the current study. Age was classified two ways: Chronological Age and Fitness Age (based on performance in fitness battery). Trunk stiffness was measured using a passive trunk flexion apparatus described in literature. Participants sat for 60 minutes at a computer workstation throughout which, at 10-minute intervals, seated spine posture, perceived low back discomfort, and muscle activity (EMG) for 6 bilateral trunk muscles were recorded. Correlations were used to assess relationships between age and stiffness, perceived discomfort, posture, and EMG variables; and ANOVAs were used to detect differences in stiffness, posture, and muscle activation between age and discomfort groups. Results: Trunk stiffness increased significantly with age, with the strongest of these relationships measured with Fitness Age (r = 0.517, p = 0.003). Age was not related to posture or to the magnitude of movement displayed while sitting; however, trunk stiffness was negatively correlated with seated spine motion (r = -0.435, p = 0.023). There was no relationship between age or discomfort with muscle activation patters. Conclusions: Age showed a positive relationship with stiffness, which was stronger when examining Fitness Age than Chronological Age. This suggests that age-related research could be improved using measures of fitness rather than age (based on year of birth). Furthermore, the decreased movement while sitting with elevated spine stiffness suggests that altered tissue material properties could affect the manner in which people perform seated work, leaving them more susceptible to the effects of static postures including reduced blood flow, nerve impingement, and associated discomfort or injuries. Further investigation is warranted to find an easy way to monitor trunk material properties in the workplace in order to control for their effect on worker health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".