PROutine: a feasibility study assessing surveillance of electronic patient reported outcomes and adherence via smartphone app in advanced cancer
Notice bibliographique
Résumé
BACKGROUND: In advanced cancer, quality of life (QoL) is a major treatment goal. In order to achieve this, the identification of suffering by screening for patient-reported-outcomes (PROs, i.e., symptoms) is of utmost importance. The use of paper-pencil questionnaires is associated with significant shortcomings due to missing data, recall bias and transcription errors. Other than that, the electronic recording of PROs by mobile Health (mHealth) offers a number of advantages. The aim of this study was to test whether the routine assessment of PROs via a newly developed smartphone application (MeQoL®) is feasible. METHODS: A prospective, uncontrolled, multi-center, feasibility trial was performed in adult outpatients with advanced, solid cancer. Patients under anti-cancer therapy and with regular outpatient visits were eligible. Patients daily recorded the degree of perceived distress (NCCN Distress Thermometer®), pain intensity {average and worst [numerical rating scale (NRS), 0-10]}, the number of breakthrough pain episodes (BPE) and ten questions from a modified version of the Edmonton Symptom Assessment Scale (ESAS). Weekly, five questions concerning different domains of QoL from the short-form 8 (SF-8) questionnaire were obtained. Also, patients recorded the intake of their opioid rescue medication. According to the main scope of the trial (feasibility), no primary endpoint was defined. Rather, the following main feasibility criteria were assessed: missing data, drop-out- and acceptance-rate, patient satisfaction, patients' judgement of practicability, patients' and physicians' suggestions for improvement and basic clinical and demographic data of the participating patients. The study was registered in the German Clinical Trials Register (ID: DRKS00008761). RESULTS: In three German cancer centers, 40 patients {female: 28 (70%); average age, 57 years [range, 27-73 years; standard deviation (SD), 12]} were included. As three devices were lost on transport, 37 devices could be evaluated. The median investigation period per device was 99.5 days (SD, 31). Patient adherence in using the smartphone app to document their distress and symptoms was high and missing data were low: In median daily reviews were performed on 70 (SD, 29) of these days (70%) and median weekly recordings were 13 weeks (87%). Most often, patients recorded symptom intensity (89%, MIDOS) and distress (85%, NCCN thermometer). On feedback forms, patients reported a good to very good user friendliness of MeQoL® and a high motivation to use this tool again. CONCLUSIONS: Even though participants were asked to record PROs rather frequently (daily), missing data were low and patient satisfaction was high. Having in mind the findings of other working groups, such routine implementation of mHealth solutions may substantially improve outcomes of cancer therapy and increase the value of trials' findings. For the individual patient, MeQoL® allows for monitoring adherence to pharmacotherapy and can facilitate patient guidance.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».