Data from the paper entitled 'Acute effects of daily life spontaneous and structured physical activity on hypoglycaemia and hyperglycaemia in children with type 1 diabetes: an observational study controlling for carbohydrate and insulin confounders'
Notice bibliographique
Résumé
In type 1 diabetes, some few studies have attempted to explore the impact of free-living self-reported physical activity (PA) sessions and the impact of accumulated spontaneous PA on hypoglycaemic risk, and less often on hyperglycaemic risk. However, no research has integrated in their analyses the confounding impact of sedentary time and/or of carbohydrate intake. Fifty-eight children/adolescents living with type 1 diabetes have worn a continuous glucose monitoring (CGM) and an accelerometer for 7 days of free-living. During these 7 consecutive days, helped by an adult, children/adolescents reported scheduled and unscheduled PA sessions, specifying the exact PA and its time, duration and subjective intensity. For each meal/snack, the participants recorded the exact time, type, amount of food consumed and insulin dose. Linear mixed models or binary/multinomial logistic regressions with random effects were used to assess the effect of free-living PA on glycaemic metrics, controlling for carbohydrate intake, insulin administration and participants characteristics. Two sets of analyses were performed, as described in details in the paper: in the 1st analyses, we explored the effect of self-reported PA sessions on glycaemic metrics during the sessions, during two hours post-sessions and from two hours post-dinner to breakfast the next day. In the 2nd set of analyses, we explored the effect of cumulative PA and sedentary time (recorded with accelerometry) considered over different periods of the day, on concomitant or subsequent glycaemic (CGM) outcomes. The data made available on recherche.data.gouv correspond to all the data files used for these 1st and 2nd sets of analyses. Access to raw data of CGM and accelerometry would also be possible upon request to Elsa Heyman and Angéline Melin. Data from 45 children/adolescents (21 females, 11‧7±3‧4years) were included. Accumulating moderate-to-vigorous PA (e>−0‧18, P<0‧05) or reducing sedentary time (OR=1‧01, P<0‧01) reduced day-time risk of >13‧9mmol.L-1 hyperglycaemia. Time spent >10‧0mmol.L-1 during self-reported PA sessions tended to decrease when children consumed less high-glycaemic index carbohydrates during the previous hour (e>+0‧40, P<0‧06) or performed other sessions before the current one (e<−11‧40, P<0‧05). Conversely, the latter conditions did not influence hypoglycaemic risk. The probability of spending time <3‧0mmol.L-1 during the night increased when children performed structured PA sessions of longer duration (OR=1‧02, P<0‧05) as well as when more vigorous-intensity PA was accumulated over the afternoon (OR=1‧07, P<0‧01). The detailed results for all analyses are fully described in the paper. In conclusion, increasing spontaneous active behaviours could help to reduce day-time level 2 hyperglycaemia. For children experiencing hyperglycaemia during exercise it could be advised to avoid excessive pre-exercise carbohydrate intake or multiply daily sessions number. However, as only characteristics of PA sessions (and not conditions under which they were undertaken) predicted risk of level 2 hypoglycaemia (nocturnal), simplified guidelines about hypoglycaemic risk could be developed.
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 | 0,005 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».