Target trial emulation of physical activity and cardiovascular disease risk: What is impact of the exposure assessment method?
Notice bibliographique
Résumé
Background: Target trial emulation (TTE) designs provide a framework for strengthening causal inference in observational research, but it is unknown how vulnerable they are to substantial error when the emulated intervention (exposure) measurement is imprecise. In physical activity epidemiology specifically, correcting for confounding via TTE designs but not addressing the large measurement errors arising from self-reports (e.g. questionnaires, which typically capture partial behavioural accounts with very low precision) creates uncertainty about possible dominance of type 2 error biases arising from such novel designs. High-resolution wearables-based methods capture most movement, providing an assessment of physical activity behaviour with substantially less, empirically verified, measurement error. No study has examined how physical activity measurement method influences causal inference in TTE studies. Objectives: We applied TTE methodology to sub-samples of the UK Biobank cohort with repeat exposure measurements, to compare the effects of an emulated physical activity intervention on incident CVD risk, when the physical activity exposure was quantified using self-report vs. wearable devices. Methods: The emulated randomized controlled trial identified physically inactive adults (<150 moderate-to-vigorous physical activity (MVPA) mins/week) who had repeat assessments for wearable and self-reported physical activity. At re-examination, participants were categorised into intervention (adopted the current recommendation of ≥150 MVPA mins/week) or control (remained physically inactive) groups. Participants in each group were propensity score-matched to balance lifestyle behaviours, demographic, and health factors. Cumulative risk for CVD incidence was assessed through cumulative risk curves, hazard ratios, risk ratios, using Fine-Gray subdistribution and Poisson regression models. Results: The wearables analytic sample included 490 participants (245 per arm; mean incident CVD follow-up 4.4 years), and the self-report sample included 11,302 participants (5,651 per arm; mean follow-up 6.3 years). In wearables assessments, guideline-adherent participants had markedly lower cumulative CVD risk (cumulative risk = 8.0% vs. 17.0%; hazard ratio [95%CI] = 0.59 [0.36, 0.98]; relative risk = 0.45 [0.28, 0.72]). In contrast, self-report assessments showed near-identical risk trajectories for intervention and control groups (cumulative risk = 21.6% vs. 21.2%; hazard ratio = 0.98 [0.89, 1.08]; relative risk = 0.92 [0.84, 1.00]). Matching the self-report sample to the wearables sample for lifestyle, demographic, and health factors confirmed these findings. Conclusion: Reliance on self-reported measures of physical activity in TTE studies may obscure emulated intervention effects due to non-differential misclassification, increasing considerably risk of Type II error. Exposure assessment using wearable devices may be essential for valid causal inference in TTE studies of physical activity and CVD risk. Future TTE studies of physical activity exposures should prioritise objective measurements to avoid biased inferences that could affect public health policy and guidelines.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,532 | 0,738 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,016 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».