Wearable devices may indeed have racial bias in health monitoring. This bias is mainly reflected in the following aspects: 1. **Underrepresentative data**: The development and testing data of many wearable devices are mainly based on specific populations, usually white or middle-class groups. This may cause the device to perform poorly among users of other races or economic backgrounds, affecting the accuracy and practicality of the data. 2. **Algorithm bias**: Wearable devices usually rely on algorithms to analyze data. If the data samples used in the training of these algorithms do not cover the diversity of different races, they may show bias in the analysis results. This may lead to the underestimation or misdiagnosis of health problems of users of different races. 3. **Cultural differences**: Different races and cultural backgrounds may have different lifestyles and health standards, and some wearable devices may not fully consider these differences, resulting in reduced accuracy of monitoring results. 4. **Socioeconomic factors**: The popularity of wearable device use is also affected by socioeconomic factors. Some racial groups may use these devices less for economic reasons, which may bias the data and research results of health monitoring. 5. **Privacy and trust issues**: Certain racial groups may be reluctant to use wearable devices due to historical distrust or privacy issues, which further limits the popularity and effectiveness of devices among these groups. To address these issues, researchers and developers should work to increase the diversity of testing and development samples and take racial and cultural differences into account when designing algorithms to ensure that wearable devices can serve all users fairly.