Gait Speed and Mood, Cognition, and Quality of Life in Older Adults With Atrial Fibrillation
Bibliographic record
Abstract
Background Low gait speed has been linked with impaired mood, cognition, and quality of life ( QOL ) in older adults. We examined whether low gait speed was associated with impaired mood, cognition, and QOL among older adults with atrial fibrillation ( AF ). Methods and Results Participants (n=1185) had a diagnosis of AF , aged ≥65 years, CHA 2 DS 2 VAS c ≥2 and had no contraindications to anticoagulation. Participants completed a 15‐foot walk test, and low gait speed was categorized using cutoffs from the Fried Frailty Index. Participants self‐reported measures of depressive symptoms (Patient Health Questionnaire 9 ≥10), anxiety symptoms (Generalized Anxiety Disorder 7 ≥10), cognitive impairment (Montreal Cognitive Assessment ≤23), and potentially impaired Atrial Fibrillation Effect Quality‐of‐Life Questionnaire <80. Participants were on average aged 75.3 ( SD : 7.0) years, 48.0% were women, and 85.5% were non‐Hispanic white; 85.6% were taking an oral anticoagulant, 26.1% had low gait speed, 8.4% had elevated depressive symptoms, 5.7% had elevated anxiety symptoms, 41.1% were cognitively impaired, and 41.6% had potentially impaired AF ‐related QOL . Participants with low gait speed were significantly more likely to have elevated depressive symptoms (adjusted odds ratio: 2.1, 95% CI : 1.3–3.4), elevated anxiety symptoms (adjusted odds ratio: 2.2, 95% CI : 1.2–3.9), and cognitive impairment (adjusted odds ratio: 1.5, 95% CI : 1.1–2.1). Impaired AF ‐related QOL did not differ by gait speed after adjustment for clinical characteristics (adjusted odds ratio: 1.1, 95% CI : 0.8–1.5). Conclusions Twenty‐six percent of older adults with AF had low gait speed, and low gait speed was associated with impaired mood and cognition. Further research is needed to determine whether declines in gait speed lead to impaired mood and cognition or whether these conditions develop concurrently.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".