A Virtual Versus In-Person Comparison of the Senior Fitness Test: A Randomized Crossover Trial
Bibliographic record
Abstract
Purpose: The Senior Fitness Test (SFT) is a validated tool for examining older adults’ mobility, strength, and flexibility. During the COVID-19 pandemic, when in-person training facilities were closed, there was a need for effective virtual options for assessments, including the SFT. The purpose of this study was to compare the validated SFT conducted in person versus an online virtual setting. Method: A virtual modified version of the SFT was compared to the modified in-person validated SFT. Community-dwelling older adults were randomly assigned, using a random number generator, to start in either the in-person or virtual modified SFT mode of delivery. After completion of the first mode of delivery (i.e., either in-person or virtual), participants completed the second mode of delivery. Results: Forty participants (50% women; mean age 72 [SD 4] years) showed no differences between the in-person and virtual delivery measurements in the 2-minute step (in person mean 87.9 [SD 18.5]; virtual mean 87.2 [SD 20.7]; p = 0.65), 30-second arm curl (in person mean 16.9 [SD 4]; virtual mean 16.5 [SD 4]; p = 0.43), 30-second chair stand in person mean 15.6 [SD 5]; virtual mean 15.2 [SD 4]; p = 0.36), and chair sit and reach (in person mean 1.2 [SD 15]; virtual mean 4.2 [SD 11]; p = 0.06). Conclusions: Performing the modified SFT in a virtual setting may be a useful delivery mode for seniors and health care professionals if in-person testing is not viable.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".