Is Health Wearables Data Accurate?
The collection and monitoring of health data through wearable devices is a powerful tool for promoting healthy behaviors. It can also help to identify and address potential disease states.
However, many people are skeptical of the value of digital health tools and are reluctant to share their data with healthcare professionals. Identifying the sociodemographic and health-related correlates of willingness to share wearable data may support the development of successful digital health interventions that incorporate these technologies.
Can we trust wearables to watch our heart health?
Despite a growing number of options, it can be difficult to figure out what each device is actually measuring. The prevailing technology involves shining a broad spectrum of light on the skin and evaluating changes in the reflected signal.
Some devices, like the Apple Watch, are moving beyond optical sensing to incorporate an electric ECG (electrocardiogram), which is far more accurate than standard heart rate tracking. But these technologies haven’t yet been tested in real patients.
In addition, users may be prone to misinterpret the data they receive and rely on it for self-diagnosis, which could lead to unnecessary anxiety or misguided self-medication. Future research could assess the determinants of attitudes about wearables and their potential to improve disease surveillance, including whether they differ based on the recipient of the data.
Are they clinically validated?
Many wearables are marketed as medical devices and must undergo testing to ensure they accurately measure what they claim, like heart rate or activity levels. However, there is no defined definition of clinical validation for wearable technologies. For example, a device’s software may change over time, and each individual’s resting heart rate is different, making comparisons to standard measurements hard.
Moreover, data are prone to biases from user behavior and factors that influence their usage. For example, people who share their digital health information on social networks or within web-based community health programs might be more likely to report higher levels of willingness to use their wearable data for disease surveillance.
Further research into the impact of these factors on willingness to use wearables in population health would be beneficial. So too would the development of analytical and clinical validation methods to guide the use of wearables in mHealth trials. Such validation could include evaluating whether a device measures what it claims to measure in the pathologic population and compares results to a mat-based gold standard.
Are they as accurate as a medical test?
Wearable devices are able to collect a huge amount of data on users. But, the question is, is it accurate?
For example, the color of a person’s skin and tattoos can interfere with the accuracy of heart-rate monitors. And the fit of a device can affect whether or not it accurately tracks sleep patterns.
Moreover, some of the information collected by wearables is sensitive and could violate participants’ privacy. And, despite 2023 updates to the US Health Insurance Portability and Accountability Act (HIPAA), concerns over reidentification remain.
Nevertheless, if healthcare companies, researchers and physicians can find ways to ensure that wearables are accurately used and validated for medical purposes, the results will revolutionize telemedicine. This will allow patients to get directional advice from devices and reduce staff workload, thus improving the patient experience in hospitals and clinics. In addition, the technology may help to prevent some dangerous situations like cardiac or respiratory arrest by monitoring conditions in remote locations.
Are they helpful?
Health wearables can help people optimise their sleep, workouts, diet and daily activities and improve their safety with alerts when metrics are outside of the expected range. But, like any technology, it can also be harmful if used incorrectly.
One major concern voiced by healthcare professionals, and reflected in this month’s M3 Pulse results, is that people could rely on the data from their device to self-diagnose or misinterpret health problems. This may lead to unnecessary anxiety or misguided self-medication.
Moreover, it’s not yet known whether the same digital health behavior correlates with both the intention and the actual use of wearables. Future mHealth interventions that involve the use of wearables should explore these factors so that they can effectively inform the design of the digital tools and how they are implemented in clinical settings. This will ensure that the potential of these technologies is maximized. It will also help to identify the most effective ways to encourage use of these devices by those with low intentions.
OnePhenix is the only IPAAS software that connects your wearable data to your healthcare professionals. www.Onephenix.com.au