Videoconference assessment of functional and cognitive measures in Brazilian older adults: a reliability and feasibility study
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine the feasibility and reliability of videoconference assessment of functional and cognitive status among older adults in the context of the COVID-19 pandemic. METHODS: Thirty community-dwelling older adults (86.70% women) with a mean age of 69.77 (SD = 6.60) years who were physically independent and had no signs of cognitive impairment were included in the sample. An independent and experienced researcher assessed functional (chair rise test, chair stand test, sitting and rising test) and cognitive (Montreal Cognitive Assessment, parts A and B of the Trail Making Test, the Stroop test, the verbal fluency test) performance in real-time on the Google Meet platform on 2 non-consecutive days. The reliability of the measures was analyzed using the intraclass correlation coefficient (ICC), a paired t-test, or Wilcoxon and Bland-Altman analysis. The feasibility of the assessment was investigated using a standardized 14-item questionnaire. RESULTS: All functional performance measures showed excellent intra-rater reliability, with ICCs from 0.90 (95%CI 0.78 – 0.95) for the sitting and rising test to 0.98 (95%CI 0.96 – 0.99) for the chair rise test. Our analysis also showed mixed levels of reliability across measures, including good ICC (ranging from 0.79 – 0.91) for the Montreal Cognitive Assessment, part B of the Trail Making Test, and the congruent and neutral trials in the Stroop test, but poor-to-moderate ICC (ranging from 0.42 – 0.58) for the other cognitive assessments. In general, the participants reported good feasibility for the assessment format. CONCLUSION: In healthy and highly educated older adults, videoconferencing is a feasible method of determining functional and cognitive performance. Functional measures showed excellent reliability indexes, whereas cognitive data should be interpreted carefully, since the reliability varied from poor to moderate.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".