Rollator usability from a nursing science perspective: A parallel qualitative content analysis of customer reviews on Amazon
Bibliographic record
Abstract
Background: Care dependency and mobility restrictions often go hand in hand, increasing the risk of falls. Rollators are essential assistive devices that support individuals' mobility and functioning, with globally varying usage rates. While overall user satisfaction is rated high, usability challenges persist. Beyond advocating for user needs, nursing science should also address the role of rollators in enabling individuals to remain at home despite care needs, ensuring safety, and shaping informal and formal care settings. Purpose: This study examined usability aspects with a strong user-centred emphasis on subjective rollator satisfaction, using Amazon rollator reviews as data source. The aim was to validate previously found aspects and to explore unknown elements of human-rollator interaction. Methods: A total 1.026 rollator reviews from three price categories (200 €) were analysed. A Qualitative Content Analysis was employed, combining deductive and inductive coding methods. Deductive analysis was conducted based on the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST 2.0). Inductive analysis was based on the Grounded Theory Method to identify themes that were not fully considered in the deductive coding. Results: A total of 2243 deductive codes were assigned, with Ease of Use (489) and Comfort (458) being the most frequently coded categories. The inductive analysis revealed that users' expectations differ depending on the objectives of the primary and secondary users, who often make the purchasing decisions. Aesthetic appeal influenced rollator acceptance and reducing stigma. Usability has been shown to evolve over time, with experience, adaptation, and wear affecting long-term satisfaction and maintenance needs. Conclusions: This study highlights the complexity of rollator usability, shaped by material and non-materialistic user needs. Amazon reviews offer valuable insights, including secondary user perspectives. Nurses can play a key role in training and advising on rollator, contributing to better provision. As rollators shape care situations, it is essential for nursing science to address assistive technology to improve usability, safety, and overall quality of care. Study Registration: Not registered.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".