Helping Individuals
Take Charge of Their Health

The N-of-1 app provides an easy-to-use tool to help people rigorously test the impact of different interventions.

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Helping Individuals Take Charge of Their Health

Nearly one in three people in the United States track their fitness in some way, either with fitness bands, smart watches, smart phone apps, or home blood pressure and other monitors.

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Helping Individuals Take Charge of Their Health

Nearly one in three people in the United States track their fitness in some way, either with fitness bands, smart watches, smartphone 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. Those observations may be interesting, but how meaningful are they? Do they, and can they, actually lead to better health or insights into our own well-being?

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 meditation to improve sleep — in a statistically rigorous way. You can find out what works best for you, which may 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 a single person monitors the effects of different treatments on themselves. Such experiments can take many forms.

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What is an N-of-1 study?

In the most basic sense, an N-of-1 study is one in which a single person monitors the effects of different treatments on themselves. Such experiments can take many forms. 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 from those interventions. For example, in a study comparing melatonin and meditation for sleep, the individual might take melatonin for a week, then meditate for a week and repeat the sequence, tracking sleep duration and quality each night. They would then see, after the entire study is over, if either intervention helped them sleep better.

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

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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 how you slept with 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 from night to night that no obvious pattern emerges. Short-term and potentially noisy data make it difficult to draw firm 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 crossover 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.

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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 with 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 leverage this variability, determining what works best for an individual in a real-life setting.

The N-of-1 approach can be particularly useful where limited evidence exists for a particular treatment. Dietary supplements, for example, are seldom subject to rigorous testing. Small studies may hint that a supplement helps a specific condition — say magnesium for headaches — but there’s too little data to develop a clinical protocol for how to use them. N-of-1 trials offer a way to assess supplements for a specific individual.

The N-of-1 approach can also help overcome some of the limitations of standard clinical trials. Large trials often lack diversity, underrepresenting women, the elderly and minority ethnic groups. A treatment that worked in a restricted study population may not show the same effect outside that group. N-of-1 trials give people the opportunity to find out what works best for them, rather than relying on data from a study that does not necessarily represent them.

Moreover, clinical trials typically take place under highly controlled conditions, which don’t reflect the messy reality of clinical practice. N-of-1 trials assess how well treatments work in the real world.

How is an N-of-1 study conducted?

One of the initial N-of-1 studies that our platform will support relates to sleep. It will compare melatonin, a supplement available at most drug stores, with 10 minutes...

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How is an N-of-1 study conducted?

One of the initial N-of-1 studies that our platform will support relates to sleep. It will compare melatonin, a supplement available at most drug stores, with 10 minutes of mindfulness meditation before bed. Using data from a range of devices, like Fitbit and the Nokia Health Mate, 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.

The N-of-1 app will offer several other 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...

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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 intervention and the outcome 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 affects attention or cognitive function, someone simply needs to take a cognitive test with and without caffeine according to a prescribed schedule.

How can doctors use N-of-1 studies?

Figuring out the best treatment for an individual patient can be challenging. For most chronic diseases — heart disease, high blood pressure and diabetes, to name a few...

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How can doctors use N-of-1 studies?

Figuring out the best treatment for an individual patient can be challenging. For most chronic diseases — heart disease, high blood pressure and diabetes, to name a few — physicians have a variety of medicines and other interventions to offer their patients. How well each one works varies widely from person to person.

Physicians generally employ a trial-and-error approach to select potential therapies, relying on past experience and clinical intuition. N-of-1 trials give physicians a tool to more rigorously evaluate how well different interventions work for a particular patient. Physicians and patients can work together to figure out the issue they want to solve and what treatments to try. The N-of-1 app then outlines the parameters of the experiment, dictating when to take different drugs and what outcomes to measure.

Take the case of a doctor treating a patient with chronic headaches. She might recommend different supplements, such as magnesium, vitamin B12 or coenzyme Q10. The app will create a treatment regimen designed to compare the different options and a schedule for tracking the frequency and severity of headaches.

Physicians could also use N-of-1 trials to assess lifestyle interventions. A doctor treating a pre-diabetic patient might suggest at-home glucose monitoring paired with different diets or exercise regimens to determine which helps the patient control blood sugar most effectively.

How can N-of-1 studies be used in clinical research?

At HD2i, we hope that N-of-1 trials will provide a way to unite clinical practice and research. By combining the results of individual N-of-1 trials, physicians will be able to determine...

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How can N-of-1 studies be used in clinical research?

At HD2i, we hope that N-of-1 trials will provide a way to unite clinical practice and research. By combining the results of individual N-of-1 trials, physicians will be able to determine which treatments work best in certain subgroups of patients under realistic clinical settings. That information can in turn inform standard clinical practice.

In standard clinical trials, it can be difficult to separate true treatment effects from confounding factors, such as differences in the underlying disease or the demographics of study participants. People get headaches for different reasons, for example, but they may all be grouped together in a headache study. Testing different interventions in the same individual helps to eliminate some of those variables — and perhaps produce more reliable outcomes.

In many drug trials, even successful ones, a large fraction of participants show no benefit from the drug under study. N-of-1 trials can help assess whether a specific individual will benefit from a particular treatment. An Australian study used N-of-1 trials to compare the effectiveness of a common drug versus placebo in people with neuropathic pain and in people with ADHD. Nearly 70 percent of the neuropathic pain group showed no benefit from the drug compared with the placebo. For ADHD, that figure was roughly 40 percent.

The same study examined whether symptoms in people with osteoarthritis were better managed by a prescription anti-inflammatory drug called celecoxib or over-the-counter acetaminophen, the ingredient in Tylenol. Eighty percent of the people in the study did just as well on acetaminophen as they did on the prescription drug. This kind of information can help doctors steer their patients to the most effective treatment, potentially avoiding expensive drugs with risky side effects.