Validity Of Smartphone App To Measure Activity In Patients Post-ACL Reconstruction Or With Knee OA
Bibliographic record
Abstract
PURPOSE: Determine if myrecovery© app can be a valid proxy for activity in patients with knee osteoarthritis (OA) and ACL reconstruction (ACLR) by comparing app activity measures with self-report journals. METHODS: We prospectively selected 200 active patients (patients with OA or patients at least 9 months following ACLR), of various sex, age, sport, and activity level. Participants tracked their daily activity for 4 weeks and used the myrecovery© app for the same period. Participants were encouraged to carry their phone unless not permitted by their activity. Activities from the diaries were converted to metabolic equivalent of task (1 kcal/kg/hr) per minute (MET/min). After the 4-week period, patients competed a Marx Activity Rating Scale (MARX) to denote their activity intensity over the past month. To investigate the association between activity measures, we constructed scatterplots and used Pearson’s r for linear data. Associations were interpreted as follows; poor: < 0.2, fair: 0.2 < 0.4, moderate: 0.4 < 0.6, strong: 0.6 < 0.8, very strong: 0.8 < 1.0. RESULTS: We consented 100 OA patients (39% female; age 58 ± 8 years) and 100 post-ACLR patients (43% female; age 25 ± 8 years). The average association between MET/min and step count for the 4-week period was r = 0.51, p = 0.002 for the OA group and r = 0.40, p = 0.03 for the ACL group. The association between MET/min and MARX for the 4-week period was r = 0.01, p = 0.95 for the OA group and r = 0.45, p = 0.01 for the ACL group. CONCLUSIONS: Associations between self-reported journals (MET/min) and app data (step count) were statistically significant for both groups, however the correlation was stronger for the OA patients compared to the ACLR patients, likely due to ACLR patients’ limited ability to carry their phones during sport. There was a strong association between MET/min and MARX scores (both self-reported) in the ACLR group, but the correlation between these measures for the OA group was weak. The type of activities being performed should be considered when determining the best method of measuring exposure. An activity score like the MARX may be a more valid measure for highly active patients participating in sport, such as those undergoing ACLR, whereas a app measuring step count such as myrecovery© may be preferred for older, less active patients with OA.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".