Testing a Dashboard Intervention for Tracking Digital Social Media Activity in Clinical Care of Individuals With Mood and Anxiety Disorders: Protocol and Design Considerations for a Pragmatic Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: Mood and anxiety disorders are prevalent mental health diagnoses. Numerous studies have shown that measurement-based care, which is used to monitor patient symptoms, functioning, and treatment progress and help guide clinical decisions and collaboration on treatment goals, can improve outcomes in patients with these disorders. Including digital information regarding patients' electronic communications and social media activity is an innovative approach to augmenting measurement-based care. Recent data indicate interest and willingness from both mental health clinicians and patients to share this type of digital information in treatment sessions. However, the clinical benefit of systematically doing this has been minimally evaluated. OBJECTIVE: This study aims to develop an electronic dashboard for tracking patients' digital social activity and a protocol for a pragmatic randomized trial to test the feasibility and efficacy of using the dashboard in real-world clinical care of patients with depression or anxiety disorders. METHODS: We developed a personalized electronic dashboard that tracks patients' electronic communications and social media activity, visualizes data on these interactions through key graphics and figures, and provides a tool that can be readily integrated into routine clinical care for use by clinicians and patients during treatment sessions. We then designed a randomized trial to evaluate the feasibility and effectiveness of using the electronic dashboard in real-world care compared to treatment as usual. The trial included patients aged ≥12 years with a mood or anxiety disorder who were receiving treatment in outpatient psychiatry clinics in the Johns Hopkins Health System and the Kennedy Krieger Institute. The primary outcome includes changes in patient-rated depression symptoms. Secondary outcomes include changes in patient-rated anxiety symptoms and overall functioning. Exploratory analyses examine the impact of the intervention on measures of therapeutic alliance and the detection of clinically actionable targets. RESULTS: We successfully developed an electronic dashboard for tracking patients' electronic communications and social media activity, and we implemented a protocol for evaluating the feasibility and efficacy of using the dashboard in routine care for mood or anxiety disorders. The protocol was approved by the Johns Hopkins University School of Medicine Institutional Review Board. In this study, we report the technological, ethical, and pragmatic considerations in developing the dashboard and testing it in a real-world setting. CONCLUSIONS: The integration of an electronic dashboard to monitor digital social activity in mental health care treatment is novel. This study examines the feasibility and effectiveness of the dashboard and the challenges in implementing this protocol. The lessons learned from developing and implementing the study will inform ongoing discussions about the value of gathering collateral information on patients' digital social activity and how to do so in a way that is acceptable and clinically effective. TRIAL REGISTRATION: ClinicalTrials.gov NCT03925038; https://clinicaltrials.gov/study/NCT03925038. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63279.
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 | 0,083 | 0,097 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 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 ».