Notice bibliographique
Résumé
In their Article on global estimates and trends of insufficient physical activity in adults, Regina Guthold and colleagues (October, 2018)1Guthold R Stevens GA Riley LM Bull FC Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants.Lancet Glob Health. 2018; 6: e1077-e1086Summary Full Text Full Text PDF PubMed Scopus (1899) Google Scholar included data from nearly 2 million participants who were representative of 96% of the global population. A key finding was that about a quarter (27·5%) of adults worldwide do not get enough physical activity to meet current public health guidelines. However, this estimate was based on self-reported questionnaires and probably misrepresents the true burden of physical inactivity around the world. Large-scale datasets are increasingly used to shape public health policies and guidelines.2Althoff T Sosič R Hicks JL King AC Delp SL Leskovec J Large-scale physical activity data reveal worldwide activity inequality.Nature. 2017; 547: 336Crossref PubMed Scopus (518) Google Scholar However, conclusions drawn from these large sample sizes can be misleading if the assessment tools are inaccurate.3Brodie MA Pliner EM Ho A et al.Big data vs accurate data in health research: large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias.Med Hypotheses. 2018; 119: 32-36Summary Full Text Full Text PDF PubMed Scopus (30) Google Scholar It is well known that self-reported measures for assessing physical activity in adults result in over-reporting of time spent being active.4Prince SA Adamo KB Hamel ME Hardt J Connor Gorber S Tremblay M A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review.Int J Behav Nutr Phys Act. 2008; 5: 56Crossref PubMed Scopus (1845) Google Scholar For example, Guthold and colleagues1Guthold R Stevens GA Riley LM Bull FC Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants.Lancet Glob Health. 2018; 6: e1077-e1086Summary Full Text Full Text PDF PubMed Scopus (1899) Google Scholar reported that 29% of Canadian adults were insufficiently active on the basis of data obtained using the International Physical Activity Questionnaire. This estimate is substantially lower than data obtained by accelerometry, which shows that 85% of Canadian adults are considered inactive—a difference of over 50 percentage points.5Colley RC Garriguet D Janssen I Craig CL Clarke J Tremblay MS Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey.Health Rep. 2011; 22: 07-14PubMed Google Scholar Reliance on inaccurate data can have important implications for future resource allocation and policy decisions about physical activity. Saying that a quarter or a third of the adult population is insufficiently active for health benefits clearly does not carry the same level of interest and attention as saying that the majority of adults do not move enough. Given that physical inactivity contributes to substantial disease and economic burden, it is important to have a clear idea of the problem. The magnitude of the physical inactivity crisis is undoubtedly larger than what is reported in the paper by Guthold and colleagues. Future large-scale physical activity studies should rely on objective and standardised physical activity measurements to monitor population levels against public health benchmarks. Research-grade accelerometers provide good physical activity estimates, and emerging technologies might provide valuable information in the future. Given the growing interest in big data and large-scale, multinational studies, scientists should not forget that massive datasets do not provide better answers if the assessment tools are not accurate. I declare no competing interests. Accuracy and inequalities in physical activity researchI agree with Regina Guthold and colleagues1 and Ding Ding in the accompanying Comment2 that large-scale interventions are needed to increase physical activity. Colombia has been labelled the least active country in the world3 and Latin America and the Caribbean had some of the highest levels of insufficient physical activity in the analysis by Guthold and colleagues.1 However, far from being inactive, Colombia and Latin America are pioneers in large-scale physical activity interventions.4 Full-Text PDF Open AccessAccuracy and inequalities in physical activity research – Authors' replyWe thank Jelle Van Cauwenberg and colleagues, Jean-Philippe Chaput, and Gary O'Donovan for their comments on our recent study, in which we analysed the prevalence of insufficient physical activity in 168 countries, and estimated regional and global trends. Full-Text PDF Open AccessAccuracy and inequalities in physical activity researchRegina Guthold and colleagues1 should be congratulated on their rigorous efforts to harmonise and analyse physical activity data from 358 surveys across 168 countries. The Article highlights global patterns of physical inactivity. We agree with the authors' call to prioritise and scale up policy to increase population levels of physical activity, but we feel that their analysis overlooks important social inequalities in physical activity that need to be taken into account by policy and research. Full-Text PDF Open AccessWorldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participantsIf current trends continue, the 2025 global physical activity target (a 10% relative reduction in insufficient physical activity) will not be met. Policies to increase population levels of physical activity need to be prioritised and scaled up urgently. Full-Text PDF Open Access
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».