Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study
Bibliographic record
Abstract
BACKGROUND: The 6-minute walk test (6MWT) is a commonly used method to assess the exercise capacity of people with many health conditions, including persistent pain. However, it is conventionally performed with in-person supervision in a hospital or clinic, therefore requiring staff resources. It may also be difficult when in-person supervision is unavailable, such as during the COVID-19 pandemic, or when the person is geographically remote. A potential solution to these issues could be to use GPS to measure walking distance. OBJECTIVE: The primary aim of this study was to assess the validity of a GPS-based smartphone app to measure walking distance as an alternative to the conventional 6MWT in a population with persistent pain. The secondary aim of this study was to estimate the difference between the pain evoked by the 2 test methods. METHODS: People with persistent pain (N=36) were recruited to complete a conventional 6MWT on a 30-m shuttle track and a 6MWT assessed by a smartphone app using GPS, performed on outdoor walking circuits. Tests were performed in random order, separated by a 15-minute rest. The 95% limits of agreement were calculated using the Bland-Altman method, with a specified maximum allowable difference of 100 m. Pain was assessed using an 11-point numerical rating scale before and after each walk test. RESULTS: The mean 6-minute walk distance measured by the GPS-based smartphone app was 13.2 (SD 46; 95% CI -2.7 to 29.1) m higher than that assessed in the conventional manner. The 95% limits of agreement were 103.9 (95% CI 87.4-134.1) m and -77.6 (95% CI -107.7 to -61) m, which exceeded the maximum allowable difference. Pain increased in the conventional walk test by 1.1 (SD 1.0) points, whereas pain increased in the app test by 0.8 (SD 1.4) points. CONCLUSIONS: In individuals with persistent pain, the 2 methods of assessing the 6MWT may not be interchangeable due to limited validity. Potential reasons for the differences between the 2 methods might be attributed to the variation in track layout (shuttle track vs continuous circuit); poor GPS accuracy; deviations from the 30-m shuttle track; human variability in walking speed; and the potential impact of a first test on the second test due to fatigue, pain provocation, or a learning effect. Future research is needed to improve the accuracy of the GPS-based approach. Despite its limitations, the GPS-based 6MWT may still have value as a tool for remote monitoring that could allow individuals with persistent pain to self-administer frequent assessments of their functional capacity in their home environment.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".