The Association of Physical Activity Fragmentation with Physical Function in Older Adults: Analysis from the SITLESS Study
Bibliographic record
Abstract
The distribution of physical activity bouts through the day may provide useful information for assessing the impacts of interventions on aspects such as physical function. This study aimed to investigate the associations between physical activity fragmentation, tested using different minimum physical activity bout lengths, with physical function in older adults. The SITLESS project recruited 1360 community-dwelling participants from four European countries (≥65 years old). Physical activity fragmentation was represented as the active-to-sedentary transition probability (ASTP), the reciprocal of the average physical activity bout duration measured using ActiGraph wGT3X+ accelerometers. Four minimum bout lengths were utilised to calculate the ASTP: ≥10-s, ≥60-s, ≥120-s and ≥300-s. Physical function was assessed using the 2-min walk test (2MWT) and the composite score from the Short Physical Performance Battery (SPPB) test. Linear regression analyses, after adjusting for relevant covariates, were used to assess cross-sectional associations. After adjustment for relevant covariates, lower ASTP using ≥10-s bouts were associated with longer 2MWT distances and higher SPPB scores. Lower ASTP using ≥120-s bouts and ≥300-s bouts were associated with longer 2MWT distances but not the SPPB. Less fragmented physical activity patterns appeared to be associated with better physical function in community-dwelling older 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".