Effectiveness of the Internet of Things for Improving Working-Aged Women’s Health in High-Income Countries: Protocol for a Systematic Review and Network Meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Women often experience many unique health issues and conditions throughout their working lives. The Internet of Things (IoT) is a system of interrelated digital devices that can enable data exchanges over a network without human-to-human or human-to-computer interaction. The usage of applications and IoT in improving women's health has recently increased worldwide. However, there has been no consensus on the effectiveness of IoT in improving women's health outcomes. OBJECTIVE: This systematic review and network meta-analysis (NMA) aims to assess and synthesize the role of apps and the IoT in improving women's health and to identify the ranking of interventions for ensuring better results for each stated outcome. METHODS: Our systematic review and NMA will be conducted in accordance with the guidelines of the Cochrane Handbook. We will comprehensively search the following electronic databases: PubMed (including MEDLINE), Cochrane Central Register of Controlled Trials, Embase, Cumulative Index to Nursing and Allied Health Literature (ie, CINAHL), PsycINFO, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry, along with other resources to identify relevant randomized controlled trials that have assessed the effects of various apps and the IoT with regard to improving working-aged women's health in high-income countries. We will segment and analyze the results of the included studies based on age categories (women undergoing a preconception period, those undergoing gestational and postpartum periods, and menopausal and pre- and postmenopausal women) and the medical history (women who have a specific medical condition-eg, cancer or diabetes-and women who do not have them) separately. Two independent reviewers will perform the study selection, data extraction, and quality assessment. Our primary outcomes include health status, well-being, and quality of life. We will perform pairwise meta-analysis and NMA to estimate the direct, indirect, and relative effects of apps and the IoT on women's health outcomes. We will also assess the hierarchy of interventions, statistical inconsistencies, and certainties of evidence for each outcome. RESULTS: We plan to conduct the search in January 2023 and are currently discussing search strategies with the literature search specialists. The final report is planned for submission to a peer-reviewed journal in September 2023. CONCLUSIONS: To the best of our knowledge, this review will be the first to identify the ranking of IoT intervention for ensuring working-aged women's health outcomes. These findings may be of great use to researchers, policy makers, and others with an interest in the field. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42022384620; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=384620. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45178.
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 | 0,076 | 0,123 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,005 |
| Méta-épidémiologie (sens large) | 0,026 | 0,037 |
| Bibliométrie | 0,016 | 0,014 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,046 | 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 ».