When it comes to incorporating new technology into drug development and clinical practice, the best place to start is with the patients. This is exactly how the New England Journal of Medicine (NEJM) editorial team structured their recent Wearable Digital Health Technology article series on the clinical applications of wearables. Each article opens with an actual clinical case where wearable data was used to care for a patient. For those of us mostly dealing with wearable data behind a computer screen, this makes it real and reminds us why we do what we do.
The second article in this series discusses the use of wearable data in major depressive disorder (MDD). The opening case highlights the current challenges with self-reports and discusses how wearable data could be used to paint a more complete picture of patients with this often-debilitating condition. It's well established that subjective data are prone to biases and can be burdensome to the patient to collect. This is particularly a concern in MDD and other mental health conditions, where cognitive abilities are affected by the underlying diseases. Now, with wearable sensor data, collected passively and continuously in a patient’s everyday life, valuable insights can be captured as objective clinical outcomes.
Here is a summary of key takeaways from the NEJM article, Wearable Technology in Clinical Practice for Depressive Disorder.
There is value in using objective data to supplement subjective data in the diagnosis and monitoring of depression.
Clinician judgement and self-reporting will always be critical in the treatment of depression, but passive, longitudinal monitoring with wearable DHTs provides a more comprehensive and accurate view of a patient’s condition. The addition of wearable DHT data can support personalized treatment plans, inform the response to medication, and even help motivate and engage patients by demonstrating data of clinical improvement over time that may be difficult to notice day-to-day.
High-quality digital data can be highly relevant and complementary to traditional measures of depression severity.
The “low-level” information such as movement, physiological metrics, and communication data are used to generate clinically relevant measures like sleep, activity levels, estimates of stress, and communication patterns. The NEJM article includes a case study using deidentified patient data showing how the wearable measures of physical activity and sleep time correlated with changes in depression score over a 6-week period, illustrating how this information could be used to guide treatment.
Raw data should be required from wearable DHTs in clinical use.
Having access to the raw data facilitates cleaning to ensure high-quality signals are generating clinically relevant digital measures. Raw data access also allows for data reprocessing as algorithms are updated, and for evaluation of data by the broader scientific community as wearable DHTs are incorporated into more studies. Perhaps someday in the future when we know exactly what data features will be needed, we will have the liberty to discard the rich information preserved only by the raw data. For now, retaining raw sensor data is a critical step on the path towards the development and validation of these digital measures in mental health treatment.
When we start by trying to better understand the patient’s perspective and what’s meaningful to their function in daily life, we can identify the type of information needed to improve treatments. This is evident in evolving regulatory guidance outlining patient-focused approaches to drug development and the use of digital health technology to advance these goals. Wearable DHTs enable low-burden, real-world data capture of behavior that can yield new insights in indications like depressive disorder and complement self-reporting and clinician judgement to improve outcomes for patients.
To learn more about how medical-grade wearable devices can help advance treatments in conditions like MDD, click here to download our recent white paper, Closing the Gap in Our Understanding of Sleep Health with Fit-For-Purpose Wearable Digital Health Technologies.
