Relationship Between Resting Heart Rate and Body Weight in Fitbit
Body weight is a factor that can affect resting heart rate (RHR) in Fitbit data, but Fitbit does not directly calculate RHR based on body weight. Instead, Fitbit measures RHR independently while considering multiple physiological factors.
Factors Affecting Resting Heart Rate in Fitbit Measurements
Body Weight and RHR Relationship
- Higher body mass index (BMI) is associated with higher resting heart rate in a J-shaped pattern, with both obesity and being underweight linked to elevated RHR 1
- Body composition is identified as a stable factor that affects basal/resting heart rate 2
- In studies examining wearable device accuracy, higher BMI was statistically associated with larger error rates in heart rate measurements across multiple devices when examining broader BMI ranges (17.2-45.0 kg/m²) 2
How Fitbit Calculates RHR
- Fitbit determines RHR through direct measurement during periods of physical inactivity, particularly during sleep 2
- Nocturnal HR (during sleep) is typically lower than resting HR measured during daytime and represents the lowest physiological HR for an individual 2
- Fitbit uses a combination of HR and accelerometer data to determine daily physical activity zones rather than calculating RHR based on body weight 2
Other Factors That Influence RHR in Fitbit Data
Physiological Factors
- Age is a stable factor affecting basal/resting heart rate 2
- Sex differences influence resting heart rate, with different baseline values typically observed between men and women 2
- Cardiovascular fitness level has a strong inverse relationship with RHR - higher fitness is associated with lower RHR 2, 3
- Day-to-day variability exists in resting heart rate measurements 2
Environmental and Behavioral Factors
- Sleep quality and sleep phase significantly affect RHR measurements 2
- Body position during measurement (supine position results in lower RHR compared to sitting) 2
- Recent physical activity affects RHR based on time since activity, rest period, intensity, and duration 2
- Food intake, nicotine use, and caffeine consumption can all temporarily increase RHR 2
Clinical Implications and Accuracy Considerations
- RHR is a valid population-level biomarker of cardiorespiratory fitness, with an inverse relationship between the two 3
- Physical activity and adiposity attenuate the relationship between RHR and fitness by approximately 50% and 10%, respectively 3
- When using wearable devices like Fitbit, skin tone should be considered as it can affect the accuracy of photoplethysmography (PPG) measurements used to determine heart rate 2
- Wrist circumference, which correlates with BMI, may affect the PPG signal quality in wrist-worn devices like Fitbit 2
Summary of Fitbit's Approach
- Fitbit directly measures RHR rather than calculating it based on body weight 2
- While body weight and RHR are correlated, Fitbit's algorithms focus on detecting periods of minimal activity to measure true resting heart rate 2
- Fitbit uses HR zones for activity classification that are based on percentages of heart rate reserve (%HRR) rather than incorporating body weight into RHR calculations 2