Digitizing Survivorship Care Plans Through the POST-Treatment Health Outcomes of Cancer Survivors (POSTHOC) Mobile App: Protocol for a Phase II Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Survivorship care plans (SCPs) are provided at the completion of cancer treatment to aid in the transition from active treatment to long-term survivorship. They describe the details of a patient's diagnosis and treatment and offer recommendations for follow-up appointments, referrals, and healthy behaviors. The plans are currently paper-based and become outdated as soon as a patient's health status changes. There is a need to digitize these plans to improve their accessibility, modifiability, and longevity. With current technology, SCPs can be linked to mobile devices and activity trackers so that patients can track health behaviors and compare them to their clinical goals, taking charge of their own health. OBJECTIVE: A mobile app, POSTHOC (POST-Treatment Health Outcomes of Cancer Survivors), that digitizes the SCP was developed, with goals of integrating it with wearable technologies and electronic medical records. Herein, we are conducting a randomized controlled trial that evaluates the POSTHOC app versus the traditional SCP on total symptom burden in the early posttreatment period. METHODS: We will recruit 54 patients who have recently completed curative therapy for cancer (any type) in person and remotely. They will be randomized 2:1, POSTHOC:usual care (unblinded). Those randomized to the POSTHOC group will receive their SCP via the app and will choose to focus on nutrition or exercise for the duration of the study based on their individual plan and personal preferences. Those randomized to the control group will get a paper-based plan. At baseline, 6 weeks, and 12 weeks, we will evaluate patient-reported outcomes, including total symptom burden (web-based questionnaire), diet (24-hour Automated Self-Administered [ASA24]), and physical activity (Fitbit Charge 6 [Google LLC]). We will also collect quantitative and qualitative feedback on the usability of the app from those in the POSTHOC arm to improve the app for future implementation studies, with a specific focus on patient-provider communication. For feasibility, we will calculate the percentage of patients who used the POSTHOC app at least 3 times per week. We will use linear mixed models to evaluate the effects of the POSTHOC app versus those of usual care on other outcomes at weeks 6 and 12. RESULTS: This trial is open to accrual in the University of Maryland Medical System as of March 2024, and as of July 3, 2024, a total of 20 participants have consented. CONCLUSIONS: This study is among the first to digitize the SCP in a mobile app and test the effects of a mobile health-delivered behavioral health intervention on symptom burden in cancer survivors. Our results will provide evidence about the effects of health self-management on symptoms. This knowledge will be integral to larger randomized controlled studies, integration with the electronic medical record, and nationwide implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05499663; https://clinicaltrials.gov/ct2/show/NCT05499663. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59222.
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 | 0,029 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,004 |
| Méta-épidémiologie (sens large) | 0,009 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,074 | 0,013 |
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 ».