Acceptability, Reliability, and Validity of Virtually Administered Gait Speed Tests
Bibliographic record
Abstract
OBJECTIVES: To evaluate the acceptability, reliability (inter- and intrarater), and validity (convergent, known groups, and predictive) of virtually administered gait speed tests for community-dwelling older adults. DESIGN: A prospective cohort study was performed, tracking health outcomes for a year. SETTING AND PARTICIPANTS: The 3-m gait speed test at usual and fast pace was administered to community-dwelling older adults over Zoom. METHOD: To examine acceptability, participants completed questionnaires regarding telehealth usability and experience. Virtual gait speed tests were administered at baseline and 24 to 72 hours later to evaluate reliability. Self-report mobility measures were used to examine convergent and known-groups validity. Participants' health outcomes were tracked for a year to evaluate predictive validity. RESULTS: Sixty participants completed the baseline assessment and 52 completed the second assessment. Participants reported an overall positive experience with the test. Intraclass correlation coefficients for reliability ranged from 0.79 to 0.90. For convergent validity, correlations >0.30 were found predominantly for usual gait speed with self-report mobility measures. Both the usual- and fast-gait speed were able to discriminate between difficulty walking and gait aid use. Usual gait speed was able to predict specialist and family doctor visits and fast gait speed was able to predict rehabilitation specialist visits over 1 year. CONCLUSIONS AND IMPLICATIONS: Our findings demonstrate support for the acceptability, reliability, and validity of virtually administered gait speed tests for community-dwelling older adults. Although future studies are needed to examine the validity of virtual gait speed tests in larger and more diverse samples to improve generalizability of results, clinicians and researchers can virtually administer 3-m gait speed tests with confidence that scores are trustworthy and reflect older adults' mobility.
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".