An eHealth Application of Self-Reported Sports-Related Injuries and Illnesses in Paralympic Sport: Pilot Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: Sport participation is associated with a risk of sports-related injuries and illnesses, and Paralympic athletes' additional medical issues can be a challenge to health care providers and medical staff. However, few prospective studies have assessed sports-related injuries and illnesses in Paralympic sport (SRIIPS) over time. Advances in mobile phone technology and networking systems offer novel opportunities to develop innovative eHealth applications for collection of athletes' self-reports. Using eHealth applications for collection of self-reported SRIIPS is an unexplored area, and before initiation of full-scale research of SRIIPS, the feasibility and usability of such an approach needs to be ascertained. OBJECTIVE: The aim of this study was to perform a 4-week pilot study and (1) evaluate the monitoring feasibility and system usability of a novel eHealth application for self-reported SRIIPS and (2) report preliminary data on SRIIPS. METHODS: An eHealth application for routine collection of data from athletes was developed and adapted to Paralympic athletes. A 4-week pilot study was performed where Paralympic athletes (n=28) were asked to weekly self-report sport exposure, training load, general well-being, pain, sleep, anxiety, and possible SRIIPS. The data collection was followed by a poststudy use assessment survey. Quantitative data related to the system use (eg, completed self-reports, missing responses, and errors) were analyzed using descriptive statistics. The qualitative feasibility and usability data provided by the athletes were condensed and categorized using thematic analysis methods. RESULTS: The weekly response rate was 95%. The athletes were of the opinion that the eHealth application was usable and feasible but stated that it was not fully adapted to Paralympic athletes and their impairments. For example, it was difficult to understand how a new injury or illness should be identified when the impairment was involved. More survey items related to the impairments were requested, as the athletes perceived that injuries and illnesses often occurred because of the impairment. Options for description of multifactorial incidents including an injury, an illness, and the impairment were also insufficient. Few technical issues were encountered, but athletes with visual impairment reported usability difficulties with the speech synthesizer. An incidence rate of 1.8 injuries and 1.7 illnesses per 100 hours of athlete exposure were recorded. The weekly pain prevalence was 56% and the impairment contributed to 20% of the reported incidents. CONCLUSIONS: The novel eHealth-based application for self-reported SRIIPS developed and tested in this pilot study was generally feasible and usable. With some adaptation to accommodate Paralympic athletes' prerequisites and improved technical support for athletes with visual impairment, this application can be recommended for use in prospective studies of SRIIPS. TRIAL REGISTRATION: ClinicalTrials.gov NCT02788500; https://clinicaltrials.gov/ct2/show/NCT02788500 (Archived by WebCite at http://www.webcitation.org/6v56OqTeP).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".