Sedentary Behavior and the Use of Wearable Technology: An Editorial
Bibliographic record
Abstract
Globally, we continue to face a mounting issue of obesity combined with inactivity; sedentary behaviour is independently associated with poor health outcomes including disease and mortality. As such, exploring ways to try to reduce sedentary behaviour and decrease the risk of diseases is an important area of consideration. The role of wearable technology, such as fitness trackers, to encourage and subsequently increase physical activity is relatively well documented. These devices have been successful at encouraging populations to increase daily activity levels. While time being sedentary is often correlated with physical activity participation, this is not always the case. Therefore, it may be just as important to consider the activity an individual is not doing when evaluating health and well-being. This Editorial will summarize the importance of distinguishing between physical activity and sedentary behaviour. It will also discuss how wearable technology, in the form of fitness trackers, may be used to encourage someone to break up sedentary bouts more often. Finally, we will consider important future research directions.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".