Reliability and Validity of a Mobile Device Application for Use in Sports-Related Concussion Balance Assessment
Bibliographic record
Abstract
Background Balance assessment is necessary when evaluating athletes after a concussion. We investigated a mobile device application (app) for providing valid, reliable, and objective measures of static balance. Objectives The mobile device app would demonstrate similar test–retest reliability to force platform center of pressure (COP) sway variables and that SWAY scores and force platform COP sway variables would demonstrate good correlation coefficients. Methods Twenty-six healthy adults performed balance stances on a force platform while holding a mobile device equipped with SWAY (Sway Medical LLC) to measure postural sway based on acceleration changes detected by the mobile device's accelerometer. Participants completed four series of three 10-second stances (feet together, tandem, and single leg), twice with eyes open and twice with eyes closed. Test–retest reliability was assessed using intraclass correlation coefficients (ICC). Concurrent validity of SWAY scores and COP sway variables were determined with Pearson correlation coefficients. Results Reliability of SWAY scores was comparable to force platform results for the same test condition (ICC = 0.21–0.57). Validity showed moderate associations between SWAY scores and COP sway variables during tandem stance (r = –0.430 to –0.493). Lower SWAY scores, indicating instability, were associated with greater COP sway. Discussion The SWAY app is a valid and reliable tool when measuring balance of healthy individuals in tandem stance. Further study of clinical populations is needed prior to assessment use. Conclusion The SWAY app has potential for objective clinical and sideline evaluations of concussed athletes, although continued evaluation is needed.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".