Integrating Consumer-Grade Wearable Data Into Orthopedic Risk Stratification for Amateur Athletes: A Perspective on an Untapped Clinical Tool

Authors

• Youssef Salama, Wayne State UniversityFollow
• William Gadbois, Wayne State UniversityFollow
• Kevin Reck, Wayne State UniversityFollow
• Scott Yaekle, Wayne State UniversityFollow

Research Mentor Name

Scott Yaekle

Research Mentor Email Address

syaekle@wayne.edu

Institution / Department

Wayne State University School of Medicine

Document Type

Research Abstract

Research Type

businessinmedicine

Graduate Level Research

no

Abstract

Background:

Consumer-grade wearables have rapidly expanded beyond fitness tracking, providing continuous physiologic and biomechanical data. Metrics such as heart rate variability (HRV), sleep efficiency, and training load have been validated as markers of both recovery and overtraining. However, orthopedic care has not yet translated these data into clinical use. Given the rising burden of overuse injuries in recreational athletes, wearable-derived information may offer a practical opportunity for early detection and prevention within outpatient settings.

Methods:

A narrative perspective review of literature from 2010–2025 was performed using PubMed. Studies addressing wearable device validity, physiologic correlates of musculoskeletal injury, and training-load modeling were analyzed. Findings were synthesized to identify clinically relevant metrics, existing implementation barriers, and potential strategies for clinical integration.

Results:

Three metrics consistently correlated with musculoskeletal risk:

1. Autonomic recovery: A sustained 10–30% reduction in HRV from individual baseline reflects parasympathetic withdrawal and autonomic fatigue, associated with overtraining and soft-tissue injury risk.
2. Sleep efficiency: Suboptimal sleep (< 85% efficiency) delays recovery and increases injury incidence.
3. Load variability: Acute workload spikes >1.5× baseline increase tendinopathy and stress-fracture risk. While HRV and sleep tracking accuracy from devices such as WHOOP, Oura, and Garmin meet clinical reliability thresholds, implementation barriers persist around data interoperability and privacy.

Conclusion:

Consumer wearables provide clinically meaningful physiologic data that can augment orthopedic prevention and rehabilitation strategies. Tracking HRV, sleep, and workload patterns may enable physicians to identify early fatigue states and modify training before injury occurs. Prospective validation and standardized interpretation thresholds are needed to safely translate these tools into orthopedic practice.

Disciplines

Life Sciences | Medicine and Health Sciences

Recommended Citation

Salama, Youssef; Gadbois, William; Reck, Kevin; and Yaekle, Scott, "Integrating Consumer-Grade Wearable Data Into Orthopedic Risk Stratification for Amateur Athletes: A Perspective on an Untapped Clinical Tool" (2026). Medical Student Research Symposium. 509.
https://digitalcommons.wayne.edu/som_srs/509