Acceptance, Barriers, and Future Preferences of Mobile Health Among Patients Receiving Trauma and Orthopedic Surgical Care: Paper-Based Survey in a Prospective Multicenter Study
Bibliographic record
Abstract
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".