Nearly one in three people in the United States track their fitness in some way, be it with fitness bands, smart watches, smart phone apps, or home blood pressure and other monitors. Most of us use these devices to improve or maintain our health and fitness, collecting information on our activity level, sleep, heart rate and other factors. But how effectively are we using that data? Your Fitbit might tell you that you slept poorly after having a few drinks, or that your heart rate or blood pressure spiked after a stressful work presentation. Though those observations may be interesting, how meaningful are they? Do they actually lead to better health, or insights into our own wellbeing?

HD2I is developing a new platform to help you leverage your health data in more powerful ways. Known as an N-of-1 study, this approach allows you to compare different interventions — say melatonin or mediation to improve sleep — in a statistically rigorous way. You can find out what works best for you, which might be completely different from what works best for someone else.

What is an N-of-1 study?

In the most basic sense, an N-of-1 study is one in which an individual monitors the effects of different treatments. Such experiments can take many forms. But at HD2I, we use N-of-1 to refer to a specific type of study — a single-patient, multiple-crossover comparative effectiveness trial. That means an individual will test different interventions multiple times over the course of a single experiment and compare the outcomes. In a study comparing melatonin and meditation for sleep, for example, the individual might take melatonin for a week, then meditate for a week and repeat the sequence, tracking sleep duration and quality each night.

The concept of an N-of-1 study has been around for decades. But it’s only in recent years that ubiquitous monitoring devices, such as fitness trackers and blood pressure and glucose monitors, have made it possible for individuals to collect health data frequently and with relatively little effort. Now many of us can easily amass enough data to study ourselves with precision.

Our goal with the N-of-1 app is to help people manage their own health. Giving users a tool that leverages data they already collect can help them more effectively improve sleep, stabilize blood sugar, lower blood pressure, boost attention and address a variety of other questions.

Why do an N-of-1 study?

Conducting studies on yourself can be challenging. Say you want to know if melatonin helps you sleep better. You try it for a night or two and compare it to the previous week’s sleep. If you see no major difference, you might conclude the melatonin had no effect. But perhaps the nights you took it coincided with a stressful presentation at work, impairing your sleep. Or maybe your sleep patterns vary so much night to night that no obvious pattern emerges. Short-term and potentially noisy data make it difficult to draw strong conclusions.

The N-of-1 app can help solve that problem by laying out a framework for comparing different interventions. The app defines the parameters of the experiment, outlining what outcomes to measure, what treatments to try and when to take them. The program then uses sophisticated statistics to analyze the data you generate, determining which treatment is most effective for you. The cross-over design — using a medication or other intervention for multiple stretches — helps speed the process, shortening the time it takes to collect enough data to draw a conclusion.

What are the advantages of N-of-1 studies?

People can react very differently to medicines and other treatments, but traditional clinical trials tend to wash away this diversity. A study comparing the effectiveness of a new blood pressure medication to an existing drug might find that the group getting the new drug tends to do better than the group getting the old one. But within each group, some people will have a strong response and others none at all. The benefit of N-of-1 studies is that they capture this variability, determining what works best for the individual in a real-life setting.

How is an N-of-1 study conducted?

The initial N-of-1 sleep study compares melatonin, a supplement available at most drug stores, to 10 minutes of mindfulness meditation before bed over five weeks. Using data from a range of devices, like Fitbit and Nokia, the study monitors different features of sleep, including duration, time to fall asleep, and the amount of time spent in different sleep stages (REM, light, deep). The app reminds you when to take melatonin or when to meditate and notifies you if there were problems retrieving data from your device. At the end of five weeks, the app determines if either treatment improved sleep, and if so, how much.

Initially the N1 app offers a few studies designed and selected by our research team. In the future, users will be able customize existing studies to test specific interventions or design their own experiments from scratch.

What makes a good N-of-1 study?

Not all health questions are good candidates for the N-of-1 study format. A strong N-of-1 trial has an outcome that can be measured easily and repeatedly, such as duration of sleep, blood pressure or score on a cognitive test. The intervention itself needs to be something that can be stopped and started, such as medication. A one-time treatment, such as surgery, isn’t appropriate for an N-of-1 trial.

N-of-1 trials require a specific intervention. You might be curious to know if bad weather or low barometric pressure triggers headaches. But because weather isn’t a controllable factor you can repeat at will, it’s not well suited to N-of-1 trials.

Timescale of both the outcome and the intervention is also important. It’s difficult to assess treatments for cold or the flu, for example, because these ailments tend to resolve quickly on their own. Slow-acting interventions are problematic as well. Significant weight loss generally takes weeks or months, so assessing the effectiveness of different diets would be a lengthy process.

Fast-acting interventions are ideal for N-of-1 trials. Caffeine, for example, acts on our brains fairly quickly. To test how caffeine effects attention or cognitive function, someone simply needs to take a cognitive test with and without caffeine according to a prescribed schedule.

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