Eight-year longitudinal falls trajectories and associations with modifiable risk factors: evidence from The Irish Longitudinal Study on Ageing (TILDA)
Bibliographic record
Abstract
BACKGROUND: The label 'faller' and the associated stigma may reduce healthcare-seeking behaviours. However, falls are not inevitably progressive and many drivers are modifiable. This observational study described the 8-year longitudinal trajectories of self-reported falls in The Irish Longitudinal Study on Ageing (TILDA) and studied associations with factors, including mobility, cognition, orthostatic hypotension (OH), fear of falling (FOF) and use of antihypertensive and antidepressant medications. METHODS: Participants aged ≥50 years at each wave were categorised by whether they averaged ≥2 falls in the previous year (recurrent fallers) or not (≤1 fall). Next-wave transition probabilities were estimated with multi-state models. RESULTS: 8,157 (54.2% female) participants were included, of whom 586 reported ≥2 falls at Wave 1. Those reporting ≥2 falls in the past year had a 63% probability of moving to the more favourable state of ≤1 fall. Those reporting ≤1 fall had a 2% probability of transitioning to ≥2 falls. Besides older age and higher number of chronic conditions, factors that increased the risk of transitioning from ≤1 fall to ≥2 falls were lower Montreal Cognitive Assessment score, FOF and taking antidepressants. Conversely, male sex, higher timed up and go time, the presence of OH and being on antidepressants reduced the probability of improving from ≥2 falls to ≤1 fall. CONCLUSION: The majority of recurrent fallers experienced favourable transitions. Improvements in cognitive and psychological status, psychotropic prescribing, mobility and OH may help improve trajectories. Findings may help combat stigma associated with falling and promote preventative healthcare-seeking behaviours.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".