The impact of wearables on health monitoring is a complex issue involving technical, social, and economic factors. While wearables have the potential to improve health outcomes, they may also perpetuate existing health disparities, including racial bias. **Potential bias in wearables:** 1. **Skin color**: Skin pigments in individuals with darker skin tones may interfere with wearables’ optical sensors, resulting in reduced accuracy and reliability. This is a known issue, for example, in pulse oximetry monitoring. 2. **Muscle mass and body composition**: Wearables often rely on assumptions about body composition (e.g., fat vs. muscle mass) and movement patterns that may not apply to individuals with different body types. This can lead to inaccurate estimates of metrics such as calorie consumption, activity levels, and more. 3. **Population-specific datasets**: The datasets used to train wearable algorithms may not reflect racial and ethnic diversity, resulting in reduced model accuracy and effectiveness for marginalized groups. 4. **Socioeconomic factors**: Access to and use of wearables is influenced by socioeconomic status (SES), which is associated with racial and ethnic identity. This can lead to unequal access to health monitoring and interventions. 5. **Language and cultural barriers**: Wearable devices may not adapt to different languages or cultural environments, which can make it difficult for people to use these devices, especially for individuals with language barriers or limited health literacy. **Examples of bias in action:** 1. **Bias in Fitbit ?i Apple Watch**: Studies have shown that these devices may be less accurate in individuals with darker skin tones, which can lead to erroneous health indicators and interventions. 2. **Bias in basal metabolic rate (BMR) estimates**: One study found that the BMR equations used by some wearable devices are biased against African Americans and Hispanics, leading to incorrect estimates of calorie needs. 3. **Bias in heart disease risk assessment**: One study found that popular wearable devices may underestimate heart disease risk in certain racial and ethnic groups, which may lead to delayed or inadequate interventions. **To mitigate bias:** 1. **Data diversity and inclusion**: Ensure that the datasets used when developing wearables are diverse and reflect the characteristics of different populations. 2. **Algorithm audits**: Regularly audit and test the algorithms in wearables to look for racial and ethnic biases and take corrective actions. 3. **Cultural Competence**: Design wearables that consider diverse languages, cultural backgrounds, and socioeconomic factors. 4. **Inclusive User Testing**: Engage diverse populations in user testing and feedback mechanisms to ensure wearables meet the needs of diverse users. 5. **Regulatory Oversight**: Encourage regulators to focus on issues of bias in wearables and develop guidelines to mitigate bias and promote equity. **Conclusion**: While wearables have the potential to improve health monitoring and outcomes, they may also perpetuate existing health disparities, including racial bias. Recognizing these biases and taking proactive steps to mitigate them is critical to ensuring that the benefits of wearables are equal and equitable for all.