Accelerometer Determined Free-Living Sit-to-Stand Velocity: A Scoping Review of Methodological Developments, Participant-Level Factors, and Link to Health
Bibliographic record
Abstract
Accelerometry provides information on an individual’s habitual physical activity and postures. The transition from sit-to-stand involves the cardiovascular, neural, and muscular systems, and is linked with balance disorders when tested in laboratory conditions. The methodology for quantifying sit-to-stand velocity (STSv) in free-living conditions and the association with participant-level and health factors are unclear. A scoping review of the available literature on free-living STSv was performed to investigate methodology, participant, and health-related factors in relation to STSv. A literature search was conducted across Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier databases (initial 2,098 articles), yielding n = 15 articles that measured STSv using an accelerometer in a free-living condition or in preparation for use in free-living conditions ( n = 10 methodological, n = 5 participant/health-related factors). Sensor type, sampling rate, and filtering characteristics were all heterogeneous, while most sensors were placed on the anterior thigh. There was no consensus for algorithm development to identify sit-to-stand transitions or for the quantification of STSv. Among studies, STSv in individuals with frailty, stroke, and older adults were slower compared with healthy controls. The evidence regarding the utility of free-living STSv is limited but encouraging. The physiological and health underpinnings of preserving or improving STSv using interventional designs are highly warranted. This scoping review identifies literature gaps and recommendations for future investigations. STSv is not limited to controlled conditions only and wearable monitors provide insight into this metric during free-living condition, but harmonized sensor data collection and analytical approaches to quantifying STSv are needed.
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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 | 0.027 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".