Usability and User Experience of a Digital Platform Prototype (Healthy Bone) to Promote Pharmacological and Nonpharmacological Treatment in Patients With Osteoporosis: Mixed Methods Study
Notice bibliographique
Résumé
Background: Osteoporosis-related fractures significantly impact older adults, often leading to disability and even premature death. While pharmacological and nonpharmacological interventions are widely recommended for managing osteoporosis, adherence to these interventions remains low. To address this challenge, we developed the Healthy Bone digital platform (desktop, mobile app, and smart TV internet-based) for use in clinical settings to improve disease management and treatment adherence. It integrates a multimedia health-related behavioral change program with a patient monitoring and management system. Objective: This study aimed to evaluate the usability and user experience of the desktop version of the Healthy Bone digital platform prototype from the patients' perspective. The findings will provide valuable insights into optimizing the digital platform and enhancing its functionality. Methods: A mixed-methods study was conducted. During usability testing, patients completed tasks simulating real-world use of the platform while using a Think-Aloud approach. After each task, participants filled out an After Scenario Questionnaire to assess task satisfaction. Subsequently, participants completed the System Usability Scale (SUS) and the eHealth Usability Benchmarking Instrument (HUBBI) to measure usability quantitatively. Following this, semistructured interviews were conducted to explore participants' experiences with the platform in greater depth. Descriptive statistics were used for quantitative analysis. Qualitative data analysis involved a combined deductive and inductive approach, ensuring a comprehensive evaluation of the platform's usability and user experience. Deductive content analysis was guided by an ontology of eHealth usability components, while thematic analysis adhered to Braun and Clarke's method to identify emerging themes. Results: Seven participants evaluated the digital platform, reporting high usability with a mean overall SUS score of 87.1 (SD 13.3). Similarly, good usability was reported across all categories of the HUBBI, except for the guidance and support category, which presented moderate levels of usability (mean 3.3, SD 1.1). Patients reported high levels of task satisfaction and identified 24 unique usability issues, predominantly related to the basic system performance, interface design, and navigation and structure categories of the eHealth usability ontology. Overall, patients had positive perceptions and acceptability of the digital platform, highlighting its simplicity, accessibility, utility, and potential to empower those with osteoporosis. Barriers to usage included limited skills, lack of suitable equipment, and time, while facilitators included motivation for behavior change, health benefits, and the decrease of potential inequalities. Conclusions: This study provided valuable insights into the usability and user experience of the desktop version of the Healthy Bone digital platform prototype. These findings will play a key role in optimizing the platform to ensure it is effectively tailored to the needs of the target population. This platform adds an understanding of how various information and communication technology tools can support and benefit large numbers of osteoporosis patients in society.
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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 | 0,014 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 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 ».