The hopes and hazards of using personal health technologies in the diagnosis and prognosis of infections

Those of us running our own self-tracking initiatives will find this Lancet study interesting. What is the value of personal health trackers when it comes to diagnosis and management of infectious diseases? What are some of the pitfalls related to bias and the digital divide? Here are just a few of the highlights I found interesting:

abnormality has historically been defined by what is normal for a healthy population, rather than what is normal for an individual

Only by knowing what is normal for an individual when they are well is it possible to identify the earliest possible deviations from that normal. This is
what personal health technologies make possible, in the real world outside of a health-care setting, and in a nearly passive manner.

in Canada finding that of people who purchased their own activity tracker, just 55% were still using it, but those that were still wearing it wore it an average of 23 days in the previous month. In another study8 that gave participants a wrist wearable, approximately 25% wore the sensor for the majority of the 4-month requested monitoring period.8 A study of nearly 4·5 million insurance plan members who were offered financial incentives for achieving activity goals found that only 1·2% activated a device, but of those that did, 80% were still using the device at 6 months.9 A meta-analysis10 found that median participant retention across eight studies was only 5·5 days, and that most studies did not include a population representative of the ethnicities and diversity of the USA.

For many, a non-wearable, passive multiparametric sensor can provide the best option for longitudinal data

To aggregate findings from multiple sensors it will also be important to understand
how sensor-specific and usually proprietary algorithms for calculating metrics such as daily resting heart rate and respiration rate can differ across devices.

In fact, additional disparities in digital technology access, digital engagement, and digital health literacy, can layer on additional inequality. Even worse, the race of the person using the technology can influence its accuracy.