Usability and User Experience of a Digital Platform Prototype (Healthy Bone) to Promote Pharmacological and Nonpharmacological Treatment in Patients With Osteoporosis: Mixed Methods Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".