Physical activity levels and self-perception among patients living with chronic conditions in France: A population-based cross-sectional study using the ComPaRe cohort
Bibliographic record
Abstract
BACKGROUND: Despite the numerous health benefits associated with physical activity (PA), many patients with chronic conditions remain inactive. We hypothesise that patients often misperceive their PA level, which affects behaviour change. We aimed to assess PA levels of patients with chronic conditions using the Global Physical activity Questionnaire (GPAQ) and compare their perception of meeting WHO guidelines (150 min of moderate-to-vigorous physical activity per week) with GPAQ measurements. METHODS: We conducted a cross-sectional analysis of the GPAQ in a sample of participants recruited from the ComPaRe e-cohort, a nationwide cohort of adult patients with chronic conditions in France. We used stratified random sampling (based on age, gender and diploma) and non-responder weighting to obtain estimates representative of PA levels of chronic patients in France. Concordance between participants' perception of meeting WHO guidelines and GPAQ measurements was assessed using Cohen's kappa coefficient. RESULTS: We included 629 patients (participation rate: 65.0%). The median age was 57 [46.0-65.4] years, with 348 (55.3%) women. A total of 369 (64.2%) patients were categorised as active (>750 metabolic equivalent tasks (Mets)/week) according to the GPAQ, with PA levels increasing with age among men. A total of 55 (8.6%) participants were unable to estimate their PA level, and 186 (32.4%) misperceived their PA level (cohen's kappa coefficient of 0.38 [0.31-0.45]), with 29 (5.1%) overestimating and 157 (27.4%) underestimating their activity. CONCLUSION: Healthcare professionals should consider accurate screening for inactivity and patients' self-perception of their PA level, as both are key to delivering personalised and impactful counselling.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".