Machine learning models help identify, analyze, and predict stress in individuals and larger populations. Stress management programs use these models to help people improve their responses to stressors and thus reduce their overall stress levels.
With the expanding online fitness and wearable health device industries, there is an increased interest in health-related apps and devices; stress management programs that integrate with these apps and devices should continue to grow in size and number.
Obviously, effective stress management improves people’s quality of life. But technologies that utilize machine learning programs can analyze large amounts of (usually biometric) data to help in this task. These models can identify stress levels and sources of stress, determine which management techniques are most effective for the individual, and predict their stress levels in the future. Furthermore, these programs can recommend stress management techniques that work best for a demographic, then further refine their recommendations to the individual
Stress management programs primary run on user data. They take in self-reported stress levels and physical symptoms as well as demographic information like age and sex since, as research indicates, stress responses tend to vary along these parameters.
More sophisticated stress management programs measure biometric data like muscle tension, electrodermal activity, and so on. Some can even measure stress responses too subtle for the wearer to consciously pick up on.
When these programs can’t measure biometric data on their own, they rely on external sources, like wearable devices. Other biometric data can be utilized without being upgraded to a “smart device” version. Blood pressure monitors, for example, can provide data that users or patients can manually input.
Other important external data are habits and life events. As above, whether this information comes from other devices or is self-reported, information like sleep quality, upcoming exams, amount of exercise, mood, and eating habits are vital to an effective stress management program.
Geospatial data can provide a lot of useful information for a stress management program. A city’s air quality or a state’s average number of daylight hours, for example, have huge impacts that individuals may not think to monitor.
In addition to user privacy, any stress management program must on some level rely on user self-reporting. If they forget something or choose to downplay or even lie about their mood or habits, the entire program becomes less effective, which, in turn, discourages usage.
Additionally, there will always be individuals who have unusual or idiosyncratic responses to stressors. Good machine learning programs can recognize and respond to these individuals’ data but it may take a while.
Some leaders in the health-tech space—including Garmin, Whoop, Samsung Health, and Oura—have previously tracked stress levels using heart rate data. But the newest wearables, coming to market in late 2020 and through next year, track stress in new ways. The latest Apple Watch Series 6 ($429) uses a built-in blood oxygen monitor to sense quick and shallow breathing, which can then be used to detect anxiety or panic attacks. Fitbit’s forthcoming release, the Fitbit Sense ($330) will be the first wearable to track stress through electrodermal activity, or how well skin conducts electricity. (Sensors on the rim pick up on moisture triggered by stress.)
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