Deployment of an End-to-End Remote, Digitalized Clinical Study Protocol in COVID-19: Process Evaluation
Notice bibliographique
Résumé
BACKGROUND: The SARS-CoV-2 (COVID-19) pandemic may accelerate the adoption of digital, decentralized clinical trials. Conceptual recommendations for digitalized and remote clinical studies and technology are available to enable digitalization. Fully remote studies may break down some of the participation barriers in traditional trials. However, they add logistical complexity and offer fewer opportunities to intervene following a technical failure or adverse event. OBJECTIVE: Our group designed an end-to-end digitalized clinical study protocol, using the Food and Drug Administration (FDA)-cleared Current Health (CH) remote monitoring platform to collect symptoms and continuous physiological data of individuals recently infected with COVID-19 in the community. The purpose of this work is to provide a detailed example of an end-to-end digitalized protocol implementation based on conceptual recommendations by describing the study setup in detail, evaluating its performance, and identifying points of success and failure. METHODS: ), skin temperature, and step count, while a tablet administered symptom surveys. Data were transmitted in real time to the CH cloud-based platform and displayed in the web-based dashboard, with alerts to the study team if the wearable was not charged or worn. The study duration was up to 30 days. The time to recruit, screen, consent, set up equipment, and collect data was quantified, and advertising engagement was tracked with a web analytics service. RESULTS: Of 13 different study advertisements, 5 (38.5%) were live on social media at any one time. In total, 38 eligibility forms were completed, and 19 (50%) respondents met the eligibility criteria. Of these, 9 (47.4%) were contactable and 8 (88.9%) provided informed consent. Deployment times ranged from 22 to 110 hours, and participants set up the equipment and started transmitting vital signs within 7.6 (IQR 6.3-10) hours of delivery. The mean wearable adherence was 70% (SD 19%), and the mean daily survey adherence was 88% (SD 21%) for the 8 participants. Vital signs were in normal ranges during study participation, and symptoms decreased over time. CONCLUSIONS: Evaluation of clinical study implementation is important to capture what works and what might need to be modified. A well-calibrated approach to online advertising and enrollment can remove barriers to recruitment and lower costs but remains the most challenging part of research. Equipment was effectively and promptly shipped to participants and removed the risk of illness transmission associated with in-person encounters during a pandemic. Wearable technology incorporating continuous, clinical-grade monitoring offered an unprecedented level of detail and ecological validity. However, study planning, relationship building, and troubleshooting are more complex in the remote setting. The relevance of a study to potential participants remains key to its success.
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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 | 0,330 | 0,305 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 ».