Correlations Between Objective and Subjective Wellness Measures: an Observational Study with Cross Country Runners

Wearable fitness technology is rapidly advancing and becoming readily available through low-cost devices used in multiple sports activities. These devices can track several performance and wellness metrics such as heart rate, speed, sleep, and more. Garmin watches are widely used by cross country runners, and Garmin’s provided metrics are designed to assist the runner in appropriate training progression. However, wellness metrics such as sleep, stress levels, and body battery may be confusing to the athletes and coaches who use these devices the most. PURPOSE: To describe, analyze, and correlate Garmin user data with daily subjective survey results in cross country runners. METHODS: Personal Garmin watches (ex. Garmin Forerunner 245) were used to collect objective health data from 13 active cross-country runners (13-23 years old). Data collection lasted for 4 weeks in the summer. To collect subjective survey data, participants answered five questions per day, which included perceived sleep quality, stress levels, energy levels, and soreness levels on a 1-5 Likert scale, and a Rating of Perceived Exertion question (0-10). To collect objective health data, participants wore their watch as much as possible. Objective sleep duration(minutes), stress scores(1-100), and body battery(1-100) were collected. Participants followed their normal exercise routines. Simple linear regression models were used to determine significance. RESULTS: On average, 61.5% of participants slept more than 8 hours per night. The correlation coefficient for sleep was .57 and the R2 was .318. Objective sleep duration and perceived sleep quality were significantly correlated (90% confidence, p = .0519). On average, for each 42.8 ± 17.9 minutes of sleep, participants felt subjectively more well-rested by 1 point. Objective stress levels and perceived stress were not significantly correlated (p = .8796). Objective body battery and perceived energy levels were not significantly correlated (p = .9240). CONCLUSION: Monitoring an athlete’s individual responses to training may include using wearable devices to assist in an appropriate training progression. While some wellness metrics may be difficult to understand, monitoring sleep using Garmin devices was shown to be effective and correlated with subjective feelings of restfulness.