Digital Life Coaching During Stem Cell Transplantation: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: For patients with multiple myeloma receiving high-dose chemotherapy followed by autologous stem cell transplantation (SCT), acute life disruptions and symptom burden may lead to worsened quality of life (QOL) and increased emotional distress. Digital life coaching (DLC), whereby trained coaches deliver personalized well-being-related support via phone calls and SMS text messaging, has been shown to improve QOL among SCT survivors. However, DLC has not been investigated during the acute peri-SCT period, which is generally characterized by symptomatic exacerbations and 2-week hospitalizations. OBJECTIVE: We launched a single-arm pilot study to investigate the feasibility of patient engagement with DLC during this intensive period. METHODS: We approached English-speaking adult patients with multiple myeloma undergoing autologous SCT at our center. Enrolled patients received 16 weeks of virtual access to a life coach beginning on day -5 before SCT. Coaches used structured frameworks to help patients identify and overcome personal barriers to well-being. Patients chose the coaching topics and preferred communication styles. Our primary endpoint was ongoing DLC engagement, defined as bidirectional conversations occurring at least once every 4 weeks during the study period. Secondary endpoints were electronic patient-reported outcome assessments of QOL, distress, and sleep disturbances. RESULTS: Of the 20 patients who were screened, 17 (85%) chose to enroll and 15 (75%) underwent SCT as planned. Of these 15 patients (median age 65 years, range 50-81 years), 11 (73%) demonstrated ongoing DLC engagement. The median frequency of bidirectional conversations during the 3-month study period was once every 6.2 days (range 3.9-28 days). During index hospitalizations with median lengths of stay of 16 days (range 14-31 days), the median frequency of conversations was once every 5.3 days (range 2.7-15 days). Electronic patient-reported outcome assessments (94% adherence) demonstrated an expected QOL nadir during the second week after SCT. The prevalence of elevated distress was highest immediately before and after SCT, with 69% of patients exhibiting elevated distress on day -5 and on day +2. CONCLUSIONS: DLC may be feasible for older patients during intensive hospital-based cancer treatments such as autologous SCT for multiple myeloma. The limitations of our study include small sample size, selection bias among enrolled patients, and heterogeneity in DLC use. Based on the positive results of this pilot study, a larger phase 2 randomized study of DLC during SCT is underway to investigate the efficacy of DLC with regard to patient well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT04432818; https://clinicaltrials.gov/ct2/show/NCT04432818.
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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 | 0,015 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».