SenseWearMini and Actigraph GT3X Accelerometer Classification of Observed Sedentary and Light-Intensity Physical Activities in a Laboratory Setting
Bibliographic record
Abstract
Purpose: To compare the ability of SenseWear Mini (SWm) and Actigraph GT3X (AG3) accelerometers to differentiate between healthy adults' observed sedentary and light activities in a laboratory setting. Methods: The 22 participants (15 women, 7 men), ages 19 to 72 years, wore SWm and AG3 monitors and performed five sedentary and four light activities for 5 minutes each while observed in a laboratory setting. Performance was examined through comparisons of accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Correct identification of both types of activities was examined using area under the receiver operating characteristic curve (AUC). Results: Both monitors demonstrated excellent ability to identify sedentary activities (sensitivity>0.89). The SWm monitor was better at identifying light activities (specificity 0.61–0.71) than the AG3 monitor (specificity 0.27–0.47) and thus also showed a greater ability to correctly identify both sedentary and light activities (SWm AUC 0.84; AG3 AUC 0.62–0.73). Conclusions: SWm may be a more suitable monitor for detecting time spent in sedentary and light-intensity activities. This finding has clinical and research relevance for evaluation of time spent in lower intensity physical activities by sedentary adults.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".