Moving Beyond the Characterization of Activity Intensity Bouts as Square Waves Signals
Bibliographic record
Abstract
Wearable activity monitors provide objective estimates of time in different physical activity intensities. Each continuous stepping period is described by its length and a corresponding single intensity (in metabolic equivalents of task [METs]), creating square wave–shaped signals. We argue that physiological responses do not resemble square waves, with the purpose of this technical report to challenge this idea and use experimental data as a proof of concept and direct potential solutions to better characterize activity intensity. Healthy adults ( n = 43, 19♀; 23 ± 5 years) completed 6-min treadmill stages (five walking and five jogging/running) where oxygen consumption (3.5 ml O 2 ·kg −1 ·min −1 = 1 MET) was recorded throughout and following the cessation of stepping. The time to steady state was ∼1–1.5 min, and time back to baseline following exercise was ∼1–2 min, with faster stepping stages generally exhibiting longer durations. Instead of square waves, the duration intensity signal reflected a trapezoid shape for each stage. The METs per minute during the rise to steady state (upstroke slopes; average: 1.7–6.3 METs/min for slow walking to running) may be used to better characterize activity intensity for shorter activity bouts where steady state is not achieved (within ∼90 s). While treating each activity bout as a single intensity is a much simpler analytical procedure, characterizing each bout in a continuous manner may better reflect the true physiological responses to movement. The information provided herein may be used to improve the characterization of activity intensity, definition of bout breaks, and act as a starting point for researchers and software developers interested in using wearables to measure activity intensity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".