Comparing two moderate-to-vigorous physical activity accelerometer cut-points in older adults with neck and back disability undergoing exercise and spinal manipulation interventions
Bibliographic record
Abstract
Measuring movement through wearable accelerometers may decrease reliance on subjective tools commonly used in clinical research. Accelerometry also allows the study of movement performance over time during a person's typical activities of daily living. Different techniques can be used to analyze and reduce accelerometer data, which could impact how the data is interpreted. The purpose of the present study was to determine which variables could be impacted by setting different parameters for data analysis. We performed a secondary analysis of data gained from a clinical trial conducted on older adults (>65; M=71.1, SD=5.3) (n=100) with neck and back disabilities and compared the effects of two different cut-point sets commonly used in the analysis of older adult accelerometry data; the Matthews (2005) and Freedson Melanson, & Sirard (1998) sets. The Matthews set was found to assign significantly greater moderate-to-vigorous physical activity (MVPA) per day than the Freedson set in all comparisons. Further results from multiple analyses of dependent variables: time in (MVPA) bouts of >10min per day; mean bout length; and number of bouts per day; will be discussed. Cut-point selection can impact key variables of interest in accelerometry data. Selection of methods with relevant justification, including the impact age may have on results, should be outlined apriori and results interpreted with caution.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".