Patients’ Expectations for App-Based Therapy in Knee Osteoarthritis: User-Centered Design Approach
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) requires long-term treatment that faces significant barriers, including inadequate physiotherapy services, especially in Slovenia and comparable European countries. Mobile health apps offer a promising solution to improve accessibility and adherence to KOA treatment. objectives: This study aimed to identify expectations of patients with KOA for app-based therapy, determine the functional requirements, and assess the main barriers and benefits of using mobile apps for KOA management. It also examined these factors about demographic data (gender, age, and education level) and motivation to perform knee exercises. Methods: A mixed methods approach was used, integrating quantitative data from a structured questionnaire and qualitative data from in-depth interviews. The purposive sample comprised 82 patients with symptomatic KOA graded 1-3 on the Kellgren-Lawrence scale, excluding those with cognitive impairments, wheelchair dependency, significant comorbidities, or language barriers. Results: The analysis revealed that 53.7% (44/82) of patients preferred smartphones, while 40.2% (33/82) favored PCs for remote KOA management, citing accessibility and convenience. Exercise videos received the highest rating (µ=9.45), followed by goal setting and tracking (µ=8.95) and regular e-messages (µ=8.83). Telephone consultations with physiotherapists were also highly valued (µ=8.41). Significant differences were observed in the perceived importance of key disease information (F9=2.077; P=.04) and exercise videos (F9=2.788; P=.05) based on motivation levels but not by gender, age, or education. Perceptions of the appropriate duration of physical activity varied with motivation levels (F9=2.490; P=.02) but not with demographic factors. Men rated ease of use (4.93 vs 4.71; F1=3.961; P=.05) and the clarity of the exercise flow display higher than women. The most significant barrier was inaccurate disease information (µ=3.96), with notable differences across age groups. Younger participants (younger than 40 years) and those aged 51-60 years expressed concerns about time management and information accuracy. Patients highlighted the ability to rewatch exercises as a key app feature, while time efficiency and improved access to physiotherapists were highly valued for convenience. Enhanced communication and accurate information were essential for building trust and ensuring effective treatment. Conclusions: Mobile health apps for KOA management should be designed with a user-centered approach, prioritizing accessibility, motivation, and effective communication. Key functionalities include high-quality exercise videos, goal setting, symptom tracking, and regular electronic reminders. Mitigating user-reported barriers and integrating age-specific adaptations can enhance adherence and therapeutic outcomes. The findings highlight the potential of mobile health technologies to optimize KOA self-management and improve patient quality of life, particularly in health care systems with limited physiotherapy accessibility, such as those in Slovenia.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".