Wearable Tech Diagnoses Lyme Disease

Wearable Tech Diagnoses Lyme Disease

Wearable technology has potential to help doctors diagnose a wide range of medical conditions quickly, thanks to the wide range of physiological data that the devices can collect. There is a myriad of diseases that cause changes in the body that skin sensors can easily detect. The potential for these tools to be used for medical diagnosis is therefore considerable.

The ability of the devices to monitor the small changes that indicate an infection has been contracted could result in faster diagnoses. Faster diagnoses mean treatment can be started more promptly, reducing the harmful effects of diseases and hastening patient recovery.

One research team that has required volunteers to wear a range of wearable devices for a period of two years has shown some success in this area. The study was conducted on 43 volunteers with the aim of demonstrating whether wearable technology could be used in the early identification of disease. The results of the study have recently been published in the journal PLOS.

The study involved participants wearing a range of sensors that recorded more than 250,000 measurements each day, including peripheral capillary oxygen saturation, radiation exposure, skin temperature, heart rate, and physical activity levels.

One of the research participants contracted Lyme disease on a trip to Norway. The changes in body temperature and increased heart rate were tracked by the wearable devices, indicating an infection had been contracted.

He used the data to convince a physician that he had an infection and to prescribe antibiotics. On his return to the U.S. he had a blood test which showed he had contracted Lyme disease. The identification of Lyme Disease was quite straightforward because in the early stages of the disease the patient experiences strong changes in heart rate and develops a fever, both of which are easy to detect using skin sensors.

In other subjects the sensors were able to identify inflammatory responses, fatigue, reduced blood pressure and oxygen saturation, while also differentiating between physiological differences between insulin resistant and insulin sensitive individuals. The latter shows the potential of the devices in the early identification of Type 2 diabetes.

While the study has produced promising results, the researchers are cautious. Close monitoring of physiological signals can be extremely useful in the early detection of disease, but it is also likely to lead to many false alarms and potentially the overdiagnosis of disease.

However, in remote areas or regions with poor healthcare coverage, the devices could prove invaluable, allowing individuals to seek medical assistance before they become too ill to do so. The early warning signals of highly serious medical conditions such as heart attacks could allow life-saving treatment to be received before it is too late.

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