Feasibility of Data Collection Via Consumer-Grade Wearable Devices in Adolescent Student Athletes: Prospective Longitudinal Cohort Study
Bibliographic record
Abstract
Background: Recent advancements in sports medicine have been fueled by innovative technologies, particularly consumer-grade wearable devices like Fitbit, Apple Watch, and Garmin. These devices offer physiological and biomechanical data and hold promise for personalized, real-time, and remote assessment of athlete recovery. However, few studies have been conducted with these devices in adolescent student athletes. Objective: The primary objective of this study was to assess the feasibility of integrating consumer-grade wearable technology into injury recovery monitoring of adolescent student athletes. Methods: The study included 34 high school student athletes aged 14-18 diagnosed with either concussion or orthopedic injury, enrolled within 10 days of injury. Participants were equipped with a Fitbit Sense for continuous monitoring of physiological markers, including cardiovascular metrics, physical activity levels, and sleep patterns. Data collection extended 4-6 weeks beyond injury clearance, during which adherence rates were assessed at both hourly and daily intervals. Hourly adherence was defined as the proportion of participants with at least 1 recorded heart rate data point per hour, while daily adherence was defined as the proportion of participants with at least 1 recorded heart rate data point per 24-hour period. Results: The study demonstrated high participant adherence to wearing the device. The orthopedic injury cohort exhibited a median adherence rate of 95%, with individual rates ranging from 82% to 100%. Similarly, the concussion cohort demonstrated a median adherence rate of 93%, with adherence rates spanning from 37% to 100%. Notably, the study encountered minimal issues related to device functionality, with only 1 participant necessitating a device replacement. Conclusions: These findings demonstrate successful integration of wearable technology in data collection for adolescent student athletes recovering from sports-related injuries. However, it is important to consider current limitations, including factors that may influence data accuracy and precision. In conclusion, this feasibility study demonstrates the practicality of using consumer-grade wearable technology for the collection of physiological and biomechanical parameters in adolescent student athletes recovering from sport-related injuries. The high level of adherence highlights the potential applicability of consumer-grade wearable devices in this population. Study findings lay the foundation for future investigations with larger and more diverse cohorts to identify the utility of device metrics in identifying unique patterns of injury-specific recovery (ie, sport-related concussion). Consumer-grade wearable devices offer promise for optimizing assessment and management of injured athletes through wearable technology integration into standard clinical protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".