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Tracking health goals by event analysis

Carlos Monastyrski
March 11, 2023

Introduction

Smartwatches are becoming increasingly popular among consumers, and they are now capable of collecting a vast amount of health-related data. This data can be used to monitor health metrics of individuals and to track the progress of medical treatments. Two popular tools for collecting this information are Apple Health and Google Fit.

Unlike traditional monitoring methods, which typically involve intermittent measurements taken during medical appointments, smartwatches can collect health data continuously throughout the day. This means that healthcare professionals can access a complete and more reliable picture of an individual's health, which can be particularly useful for monitoring chronic conditions or tracking the progress of medical treatments.

In this article we describe how we collect data on sleep, exercise and meditation to track a patient's evolution of their daily goals.

Problem and Solution

In our particular situation, in one of the healthcare apps that we worked on at Ensolvers we had a series of daily goals that a patient had already previously set and some of them were closely related to this health data. The progress on these goals could be automatically tracked just by using a smartwatch, which not only makes it easier for the patient to update his/her activity, but also makes the data more reliable. From this point, the solution can be divided into two stages.

The first stage involved making a correspondence between the objectives recorded in the system and the corresponding metrics of the information that a smartwatch can record. In order to create an accurate system for calculating each metric, we conducted a thorough analysis of each one. For instance, for sleep, we recorded the total number of minutes that the user slept. For metrics such as heart rate, we calculated the average of the measurements taken within a specific time frame. For exercise, we used multiple metrics such as steps taken or total minutes of exercise detected to gain a more complete understanding of the user's activity levels.

Once this was done, we provided an API to the mobile application that gets the data synchronized with the watch, and depending on the metric, the user’s goals related to it will be updated. Different aspects had to be taken into account such as, for example, that in case of receiving the same information twice by mistake, it would be computed only once, in case of sleep values to register the minutes correctly, we had to take into account considerations such as taking any measurement after 8pm as the next day's data. An example of this is detailed below.

With this type of monitoring, the need for the patient to continuously record his or her evolution is greatly reduced and is done automatically, saving the user a lot of time and recording much more precise and real values so that the medical team has more accurate information about the patient's activity.

Conclusion

Smartwatches are becoming a popular tool for collecting health-related data, which can provide healthcare professionals with a complete and more reliable picture of a person's health. The continuous data collection offered by smartwatches can be especially useful for monitoring chronic diseases and tracking the progress of medical treatments. In the specific situation discussed, it helped to improve the user experience and engagement by using a system of daily goals and to provide a much more useful tool for doctors to be able to better track their patients and, consequently, provide them with better service.

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