Participant Experiences of a COVID-19 Virtual Clinical Study Using the Current Health Remote Monitoring Platform: Case Study and Qualitative Analysis
Notice bibliographique
Résumé
BACKGROUND: During the COVID-19 pandemic, individuals with a positive viral test were enrolled in a study, within 48 hours, to remotely monitor their vital signs to characterize disease progression and recovery. A virtual trial design was adopted to reduce risks to participants and the research community in a study titled Risk Stratification and Early Alerting Regarding COVID-19 Hospitalization (RiskSEARCH). The Food and Drug Administration-cleared Current Health platform with a wearable device is a continuous remote patient monitoring technology that supports hospital-at-home care and is used as a data collection tool. Enrolled participants wore the Current Health wearable device continuously for up to 30 days and took a daily symptom survey via a tablet that was provided. A qualitative substudy was conducted in parallel to better understand virtual trial implementation, including barriers and facilitators for participants. OBJECTIVE: This study aimed to understand the barriers and facilitators of the user experience of interacting with a virtual care platform and research team, while participating in a fully virtual study using qualitative and quantitative data. METHODS: Semistructured interviews were conducted to understand participants' experience of participating in a virtual study during a global pandemic. The schedule included their experience of enrollment and their interactions with equipment and study staff. A total of 3 RiskSEARCH participants were interviewed over telephone, and transcriptions were inductively coded and analyzed using thematic analysis. Themes were mapped onto the Theoretical Domains Framework (TDF) to identify and describe the factors that influenced study adherence. Quantitative metrics, including adherence to wearable and scheduled tasks collected as part of the RiskSEARCH main study, were paired with the interviews to present an overall picture of participation. RESULTS: All participants exceeded our definition of a fully adherent participant and reported that participation was feasible and had a low burden. The symptoms progressively resolved during the trial. Inductive thematic analysis identified 13 main themes from the interview data, which were deductively mapped onto 11 of the 14 TDF domains, highlighting barriers and facilitators for each. CONCLUSIONS: Participants in the RiskSEARCH substudy showed high levels of adherence and engagement throughout participation. Although participants experienced some challenges in setting up and maintaining the Current Health kit (eg, charging devices), they reported feeling that the requirements of participation were both reasonable and realistic. We demonstrated that the TDF can be used for inductive thematic analysis. We anticipate expanding this work in future virtual studies and trials to identify barriers and enabling factors for implementation.
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 | 0,025 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,011 | 0,010 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».