Hello, and welcome back. In this lesson, we're going to introduce the attraction and the dangers of what I'll call global metrics. I think a very exciting development in the wearable technology field is the creation of many new measures or metrics. To be fair, I worry that these metrics might be over-interpreted based on the naivety of the typical consumer, and hence the danger of these metrics. Nevertheless, it is very exciting that today's smartwatches and smart bands have the ability and the capacity to continuously measure many different aspects of our lives. To include both external and internal sensor measures, this has resulted in the capacity for these devices to compare many different measures to create their own new metrics. In many cases, these new metrics use proprietary algorithms to provide the wearer with a unique whole body readout regarding things like stress or recovery. We'll refer to these as global metrics since they are not truly or simply measures, and they are attempting to provide a more global view of what is happening to the wearer. In fact, there has been a great fascination with these new global metrics. Consumers are often captivated by these metrics, and there have been some reports of the metrics leading to behavior change to improve the reported metrics such as one's sleep score or recovery score. In fact, these metrics have provided the consumer with specific values, for example, regarding their sleep score, where people previously had only rudimentary measures such as how many hours they slept or vague feelings about their sleep quality. Some examples beyond sleep score from current wearables include stress score or a body battery value among others. Of particular interests and importance, there are reports of these metrics helping motivated athletes to change behavior in order to improve their scores. One example is a report of athletes consuming less alcohol before bed in order to boost metrics related to sleep quality, as alcohol before bed has been demonstrated to specifically reduce REM sleep. The companies that are using proprietary algorithms to evaluate sleep, stress, and energy will typically share some information about the sensors being used to calculate or predict these global metrics. The vast majority of the devices used today incorporate both internal and external measures, and compare them with historical data from the wearer. For example, how much accelerometry load has the wearer had relative to usual days? Similarly, how much time have they had with an elevated heart rate compared to historical data? Because historical data is typically a critical component of the metric, some devices will not provide the global metric values until there has been an adequate amount of data to refer to. For example, in the case of the Biostrap, the app requires five nights of wear time before they begin to provide their detailed sleep metrics. What is the real utility of these global metrics? Well, that is an important question and one that is not easily answered, is there are many different metrics available and many of them are not possible to truly validate. However, as we have mentioned, most of these metrics are calculated using the internal and external sensors we have been discussing, including: heart rate, heart rate variability, breathing rate, and accelerometry. Newer devices may also be incorporating in measures like electrodermal activity and/or pulse oximetry. Each year we have new bells and whistles added to the available wearable devices on the market, which makes for a lot of opportunity for people to attain the latest and greatest gadgets. However, it is very important to keep in mind that many or most global metrics cannot really be validated. A pet peeve of mine is that consumers in many cases, my own students in my residential wearable technology course are excited by how smart their devices are to be able to measure their body battery, for example, as if it has a sensor that links directly to our "charged" status like a cell phone does. If we step back and keep in mind the different sensors that are available to the devices that we have, then it is likely that we will be puzzled by some of the metrics that our wearables provide. For example, how do we validate the level of recovery of an athlete? What is the gold standard for measuring recovery? Similarly, how do we validate the sleep score that is provided by a wearable? We'll dig a little deeper into these ideas in our next lesson.
