Using smartwatches and genomic data to better understand psychiatric illness of children

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"Using smartwatch data collected from more than 5,000 adolescents, ... researchers were able to train AI models to predict whether individuals had different psychiatric illnesses and uncover illness-associated genes. The findings ... suggest wearable sensors may enable a much more nuanced understanding — and treatment — of psychiatric illness. ...

The data used in the study — collected from smartwatches worn by adolescents between the ages of 9 and 14 — included measurements of heart rate, calorie expenditure, activity intensity, steps taken, sleep level, and sleep intensity.  ...

The researchers trained machine learning models to predict whether an individual had attention-deficit/hyperactivity disorder (ADHD) or an anxiety disorder based on either the dynamic smartwatch data or a snapshot of the data that summarized what was collected over time. They found that digital phenotypes significantly improved model accuracy and the best models leveraged the dynamic data (rather than the snapshot), suggesting the additional temporal details were useful in characterizing illness.

Heart rate was the most important measure for predicting ADHD, the team found, while sleep quality and stage (the different cycles a body passes through during sleep) were more important for identifying anxiety. ...

Moreover, the data could also help differentiate between different subtypes of the disease.  ..."

From the highlights and abstract:

"Highlights

• Uniform processing of wearable and genomic data and integration with AI modeling and GWAS

• AI framework uses wearable digital phenotypes to better predict psychiatric disorders

• Univariate and multivariate digital phenotypes can act as a continuous response for GWAS

• Wearable GWAS detects a larger number of loci compared with traditional case-control GWAS

Summary

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion.

Here, we analyze wearable and genetic data from the Adolescent Brain Cognitive Development (ABCD) study. Leveraging >250 wearable-derived features as digital phenotypes, we show that an interpretable AI framework can objectively classify adolescents with psychiatric disorders more accurately than previously possible. To relate digital phenotypes to the underlying genetics, we show how they can be employed in univariate and multivariate genome-wide association studies (GWASs). Doing so, we identify 16 significant genetic loci and 37 psychiatric-associated genes, including ELFN1 and ADORA3, demonstrating that continuous, wearable-derived features give greater detection power than traditional case-control GWASs. Overall, we show how wearable technology can help uncover new linkages between behavior and genetics."

Using smartwatches to better understand psychiatric illness | Yale News "Continuous data collected by smartwatches can yield a much more detailed understanding of brain and behavioral illness and connect it to underlying genetics."

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations (open access)

Graphical abstract