How should we measure physical activity after stroke? An international consensus
Bibliographic record
Abstract
BACKGROUND: Physical activity is important for secondary stroke prevention. Currently, there is inconsistency of outcomes and tools used to measure physical activity following stroke. AIM: To establish internationally agreed recommendations to enable consistent measurement of post-stroke physical activity. METHODS: Stroke survivors and carers were surveyed online once regarding what is important in physical activity measurement. Three survey rounds with expert stroke researchers and clinicians were conducted using Keeney's Value-Focused Thinking Methodology. Survey 1 identified physical activity tools, outcomes, and measurement considerations which were ranked in Survey 2. Consensus recommendations on tools were then formulated by the consensus group based on survey responses. In Survey 3, participants reviewed ranked results and evidence gathered to determine their support for consensus recommendations. RESULTS: Twenty-five stroke survivors, 5 carers, 18 researchers, and 17 clinicians from 16 countries participated. Time in moderate-vigorous physical activity and step count were identified as the most important outcomes to measure. Key measurement considerations included the ability to measure across frequency, intensity, duration domains in real-world settings; user-friendliness, comfort, and ability to detect changes. Consensus recommendations included using the Actigraph, Actical, and Activ8 devices for physical activity intensity; ActivPAL for duration and Step Activity Monitor for frequency; and the IPAQ and PASE questionnaires. Survey 3 indicated 100% support for device and 96% for questionnaire recommendations. CONCLUSIONS: These consensus recommendations can guide selection of physical activity measurement tools and outcomes. Tool selection will depend on measurement purpose, user-knowledge, and resources. Comprehensive measurement requires the use of devices and questionnaires.
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 | 0.211 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".