Getting a Grip on Secular Changes: Age–Period–Cohort Modeling of Grip Strength in the English Longitudinal Study of Ageing
Bibliographic record
Abstract
BACKGROUND: Grip strength is a popular and valuable measure in studies of physical functional capabilities in old age. The influence of historical trends and differential period-specific exposures can complicate the interpretation of biomarkers of aging and health and requires careful analysis and interpretation of aging, birth cohort, and period effects. This study evaluates the effects of aging, period, and cohort on grip strength in a population of adults and older adults. METHODS: We use more than 27 000 observations for individuals at least 50 years of age, born in approximately 1910-1960, from the English Longitudinal Study of Ageing to examine a variety of multilevel and cross-classified modeling approaches to evaluate age, period, and cohort effects. Our results extended Hierarchical Age-Period-Cohort modeling and compared our results with a set of 9 submodels with explicit assumptions to determine the most reliable modeling approach. RESULTS: Findings suggest grip strength is primarily related to age, with minimal evidence of either period and/or cohort effects. Each year's increase in a person's age was associated with a 0.40-kg decrease in grip strength, though this decline differs by gender. CONCLUSIONS: We conclude that as the population ages, grip strength declines at a systematic and predictable rate equal to -0.40 kg per year (approximately -0.50 kg for men and -0.30 kg for women) in residents of England aged 50 and older. Age effects were predominant and most consistent across methodologies. While there was some evidence for cohort effects, such effects were minimal and therefore indicative that grip strength is a consistent physiological biomarker of aging.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".