Acceptance, Barriers, and Future Preferences of Mobile Health Among Patients Receiving Trauma and Orthopedic Surgical Care: Paper-Based Survey in a Prospective Multicenter Study
Notice bibliographique
Résumé
BACKGROUND: Smartphones have become an essential part of everyday life and it is undeniable that apps offer enormous opportunities for dealing with future challenges in public health. Nevertheless, the exact patient requirements for medical apps in the field of orthopedic and trauma surgery are currently unknown. OBJECTIVE: The aim of this study was to define target groups, evaluate patient requirements, and the potential and pitfalls regarding medical apps specific for patients receiving orthopedic and trauma surgical care. METHODS: A prospective multicenter study was conducted between August 2018 and December 2019 at a German trauma center and 3 trauma surgery/orthopedic practices. A paper-based survey consisting of 15 questions evaluated information regarding smartphone and medical app usage behavior. In addition, suggested app functions were rated using Likert scales. Descriptive statistics and binary log-binomial regression were performed. RESULTS: A total of 1055 questionnaires were included in our statistical analysis. Approximately 89.57% (945/1055) of the patients in this study owned a smartphone. Smartphone ownership probability decreased with every decade of life and increased with higher levels of education. Medical information was obtained via mobile web access by 62.65% (661/1055) of the patients; this correlated with smartphone ownership in regard to age and educational level. Only 11.18% (118/1055) of the patients reported previous medical app usage, and 3.50% (37/1055) of the patients received an app recommendation from a physician. More than half (594/1055, 56.30%) of the patients were unwilling to pay for a medical app. The highest rated app functions were information about medication, behavioral guidelines, and medical record archival. An improved treatment experience was reported through the suggested app features by 71.18% (751/1055) of the patients. CONCLUSIONS: Mobile devices are a widely used source of information for medical content, but only a minority of the population reported previous medical app usage. The main target group for medical apps among patients receiving orthopedic and trauma surgical care tends to be the younger population, which results in a danger of excluding fringe groups, especially the older adults. Education seems to be one of the most important pull factors to use smartphones or a mobile web connection to obtain health information. Medical apps primarily focusing on an optimized patient education and flow of information seem to have the potential to support patients in health issues, at least in their subjective perception. For future target group-oriented app developments, further evidence on the clinical application, feasibility, and acceptance of app usage are necessary in order to avoid patient endangerment and to limit socioeconomic costs.
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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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| 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 ».