Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions
Notice bibliographique
Résumé
BACKGROUND: Monitoring hydration is crucial for maintaining health and preventing dehydration. Despite the potential of wearable devices for continuous hydration monitoring, health research hasn't fully explored this application, and clear design guidelines are absent. This scoping review aimed to address this gap by analyzing current research trends and assessing the potential impact of wearable technologies for hydration monitoring. OBJECTIVE: This review comprehensively examined recent advancements in wearable hydration-monitoring technologies, focusing on their capabilities, limitations, and research and prototype designs. It explored various sensors and technologies used to track hydration, compared their advantages and disadvantages, identified trends in wearable hydration-monitoring devices, evaluated their accuracy and reliability against established benchmarks, and reviewed commercially available products to bridge research findings and practical applications. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched PubMed, IEEE Xplore, and Google Scholar for studies (2014-2024) on noninvasive wearable devices that used physiological biomarkers. Validation with human participants or comparisons with gold standards was required. Data extraction covered study characteristics, sensor technologies, validation methods, and commercial product analysis. In addition to academic research papers, gray literature was included through a Google Scholar search to investigate commercial products in the field of hydration monitoring. This approach ensured a broader perspective on technological advancements and market trends. RESULTS: The review synthesized 63 articles selected from 156 included for full-text analysis. The literature was categorized based on sensor types, including electrical, optical, thermal, microwave, and multimodal sensors. Most studies (47/63, 75%) examined the effects of hydration on physiological parameters, with some (16/63, 25%) focusing on hydration status during physical activity or in specific environmental conditions. Commercially available products from 8 companies were also evaluated for their technological features, functionalities, and applications. The dominance of electrical sensors in research was highlighted due to their ease of use and integration into wearable devices. While fewer in number, optical sensors demonstrated higher precision and provided molecular-level insights. The emergence of multimodal sensors suggests a trend toward combining technologies to improve accuracy, as reflected by their increasing publication share. Other sensors, such as thermal and microwave-based sensors, occupied specialized niches. The growing acceptance of optical-based wearables in the market reflects their cost-to-precision effectiveness. CONCLUSIONS: Wearable hydration-monitoring devices provide real-time assessments of hydration status, but challenges remain in ensuring their reliability, accuracy, and applicability across diverse populations and conditions. Future directions for research include standardized protocols, extensive clinical trials, sensor miniaturization, and enhanced wearability. Multimodal systems that integrate various sensors with artificial intelligence-driven analysis hold promise for personalized hydration management. This review offers detailed insights into the strengths and challenges of sensor technologies, paving the way for practical skin hydration-monitoring solutions.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,000 |
| 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 ».