Digital Self-Monitoring Tools for the Management of Gestational Weight Gain: Protocol for a Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Gestational weight gain (GWG) exceeding the recommendations of the Institute of Medicine (in the United States) is associated with numerous adverse maternal and infant health outcomes. While many behavioral interventions targeting nutrition and physical activity have been developed to promote GWG within the Institute of Medicine guidelines, engagement and results are variable. Technology-mediated interventions can potentially increase the feasibility, acceptability, and reach of interventions, particularly for pregnant women, for whom integration of interventions into daily life may be critical to retention and adherence. Previous reviews highlight GWG self-monitoring as a common intervention component, and emerging work has begun to integrate digital self-monitoring into technology-mediated interventions. With rapid advances in technology-mediated interventions, a focused synthesis of literature examining the role of digital self-monitoring tools in managing GWG is warranted to guide clinical practice and inform future studies. OBJECTIVE: The proposed review aims to synthesize the emerging research base evaluating digital GWG self-monitoring interventions, primarily focusing on whether the intervention is effective in managing GWG. Depending on the characteristics of the included research, secondary focus areas will comprise intervention recruitment and retention, feasibility, acceptability, and differences between stand-alone and multicomponent interventions. METHODS: This protocol was developed following the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines for systematic review protocols. The proposed review would use a planned and systematic approach to identify, evaluate, and synthesize relevant and recent empirical quantitative studies (reported in English) examining the use of digital weight self-monitoring tools in the context of technology-mediated interventions to manage GWG in pregnant US adults, with at least 2 instances of data collection. Literature eligible for inclusion will have a publication date between January 2010 and July 2020. The Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies will be used to assess the methodological quality of included studies across various domains, and results will be synthesized and summarized per the synthesis without meta-analysis guidelines. RESULTS: The initial queries of 1150 records have been executed and papers have been screened for inclusion. Data extractions are expected to be finished by December 2023. Results are expected in 2024. The systematic review that will be generated from this protocol will offer evidence for the use of digital self-monitoring tools in the management of GWG. CONCLUSIONS: The planned, focused synthesis of relevant literature has the potential to inform the use of digital weight self-monitoring tools in the context of future technology-mediated interventions to manage GWG. In addition, the planned review has the potential to contribute as part of a broader movement in research toward empirically supporting the inclusion of specific components within more extensive, multicomponent interventions to balance parsimony and effectiveness. TRIAL REGISTRATION: PROSPERO CRD42020204820; https://tinyurl.com/ybzt6bvr. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50145.
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 | 0,090 | 0,135 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,006 |
| Méta-épidémiologie (sens large) | 0,017 | 0,016 |
| Bibliométrie | 0,013 | 0,014 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,011 |
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 ».