Sensor devices such as activity monitors, heart rate monitors, sleep trackers, continuous glucose monitors, and even noise or light sensors, are increasingly becoming part of routine health care. There are of course still significant technical challenges: How does a continuous stream of everyday observations get integrated into one's electronic medical record? How does it get processed into a form that clinicians can use? What are the algorithms that turn data into information for the end users of these devices, and are they accurate? Is advice based on these observations reliable? Does it ultimately help to treat or prevent health problems for the people who use the devices?
Despite these open questions, monitoring devices are becoming more varied and feature-rich every day, and we can expect their use to continue growing. In the current blog post, then, I will consider monitoring devices from a psychological perspective. My question of interest is not whether the devices help people (the average patient seems relatively convinced that they do). Instead, I'm interested in how sensor devices might help people to improve their health -- the various possible mechanisms of action by which using a sensor device might lead someone to health behavior change. To consider this question I will rely on a relatively simple example of health sensors: the MEMS cap. MEMS (Medication Event Monitoring System) are large plastic caps that get screwed on to the top of a pill bottle -- see the image above. MEMS caps record a series of date/time stamps, which allow one to infer the pattern of times when someone opened the bottle to take their medication. People who use MEMS or other electronic monitoring devices tend to have better adherence, at least for a little while.
Why should that be the case? All the device is doing is to track someone's medication use. It doesn't include alarms, or text notifications, or other features that might directly remind people to take their medication. Its effect is therefore entirely psychological. Here are some ways in which it might help:
1. When you give someone a monitoring device, it can make them start to pay more attention to their own behavior. Mere attention can have strong effects, because it brings things to people's awareness that they didn't notice previously. Simply paying attention to something can also increase someone's level of motivation to change it. Essentially, the behavior becomes something that they are thinking about, rather than something that was unconscious or invisible to them.
2. If the device lets you see your own data, that can enhance the effect of attention. Again, viewing your results can help you to be aware of patterns or make you more conscientious about things that were previously outside your awareness. Providing people with their own data is the first step in implementing gamification strategies like streak tracking or badges, but it can be beneficial even without those additions.
3. When people know someone else is paying attention to their behavior, they tend to behave differently. This is called a monitoring effect or a measurement effect. Knowing that someone else is paying attention to you can have a powerful motivating effect, making you want to please them.
4. If health care providers have new information about their patients' behavior, they may change their clinical approach. For example, knowing that a patient is taking their medication consistently without any benefit may lead the prescriber to change the treatment plan or increase the dose. That's different from what they might have done if they suspected that the treatment wasn't working because of patient nonadherence.
Starting from these monitoring basics, one can add many different enhancements. Besides the various gamification strategies, it's possible to provide tailored messages to patients. These messages can themselves affect behavior via different pathways, such as direct information to increase patients' knowledge, motivational messages to increase their readiness for change, or feedback about their progress toward health goals over time. Alternately, the pathway summarizing sensor data for healthcare providers can feed into decision support algorithms that recommend specific evidence-based changes in treatment, or even suggest evidence-based ways to interact with patients in support of behavior change (e.g., motivational interviewing). And more sophisticated sensors than MEMS provide correspondingly more options, e.g. based on combining data from movement, heart rate, noise, and temperature sensors. All of these options build on each other to create a health care system that is more individualized and responsive to patients' needs, while also offering patients needed scaffolding to change their own health behaviors.
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