A Web-Based Mobile App (INTERACCT App) for Adolescents Undergoing Cancer and Hematopoietic Stem Cell Transplantation Aftercare to Improve the Quality of Medical Information for Clinicians: Observational Study
Notice bibliographique
Résumé
BACKGROUND: A growing number of cancer and hematopoietic stem cell transplant (HSCT) survivors require long-term follow-up with optimal communication schemes, and patients' compliance is crucial. Adolescents have various unmet needs. Regarding self-report of symptoms and health status, users of mobile apps showed enhanced compliance. Currently, HSCT aftercare at the HSCT outpatient clinic of the St. Anna Children's Hospital in Vienna, Austria, is based on handwritten diaries, carrying various disadvantages. Recently, we developed the prototype of a web-based, self-monitoring gamified mobile app tailored for adolescents: the INTERACCT (Integrating Entertainment and Reaction Assessment into Child Cancer Therapy) app. OBJECTIVE: This observational, prospective study evaluated the usability of the INTERACCT app for tracking real-time self-reported symptoms and health status data in adolescent HSCT patients and a healthy matched control group. The primary outcome of the study was the quality of the self-reported medical information. We hypothesized that the mobile app would provide superior medical information for the clinicians than would the handwritten diaries. METHODS: Health data were reported via paper diary and mobile app for 5 consecutive days each. The quality of medical information was rated on a 5-point scale independently and blinded by two HSCT clinicians, and the duration of use was evaluated. A total of 52 participant questionnaires were assessed for gaming patterns and device preferences, self-efficacy, users' satisfaction, acceptability, and suggestions for improvement of the mobile app. Interrater reliability was calculated with the intraclass correlation coefficient, based on a two-way mixed model; one-way repeated-measures analysis of variance and t tests were conducted post hoc. Descriptive methods were used for correlation with participants' demographics. For users' satisfaction and acceptability of the mobile app, the median and the IQR were calculated. RESULTS: Data from 42 participants-15 patients and 27 healthy students-with comparable demographics were evaluated. The results of our study indicated a superiority of the quality of self-reported medical data in the INTERACCT app over traditional paper-and-pencil assessment (mobile app: 4.14 points, vs paper-based diary: 3.77 points, P=.02). The mobile app outperformed paper-and-pencil assessments mainly among the patients, in particular among patients with treatment-associated complications (mobile app: 4.43 points, vs paper-based diary: 3.73 points, P=.01). The mobile app was used significantly longer by adolescents (≥14 years: 4.57 days, vs ≤13 years: 3.14 days, P=.03) and females (4.76 days for females vs 2.95 days for males, P=.004). This corresponds with a longer duration of use among impaired patients with comorbidities. User satisfaction and acceptability ratings for the mobile app were high across all groups, but adherence to entering a large amount of data decreased over time. Based on our results, we developed a case vignette of the target group. CONCLUSIONS: Our study was the first to show that the quality of patient-reported medical information submitted via the INTERACCT app embedded in a serious game is superior to that submitted via a handwritten diary. In light of these results, a refinement of the mobile app supported by a machine learning approach is planned within an international research project.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».