How Continuous Behavioral Health Measurement Enhances Real-world Data & Evidence

Continuous Behavioral Health Measurement is an emergent opportunity to produce richer real-world data & evidence. 

Real-world data and evidence have been defined by the US Food & Drug Administration (FDA) and its use in health care decision making has been increasingly encouraged. 

Real-World Data 

According to the FDA, real-world data (RWD) are “data relating to patient health status and or the delivery of healthcare routinely collected from a variety of sources”. These sources may include electronic health records, claims and billing information, product and disease registries, patient-generated data and mobile device sensor-generated data.  

Real-World Evidence 

Real-world evidence (RWE) is clinical evidence regarding the use and potential benefits, or risks, of a medical product derived from the analysis of real-world data. This evidence is generated from various study designs or analyses that are inclusive of randomized trials and observational studies. 

The Importance of Real-World Data and Evidence 

This kind of data and evidence is important because it can be generated and collected passively in the naturalistic everyday lives of patients and clients versus controlled environments like laboratories. Lab results often yield different results than the results gathered from real-world use of products and services.  

The availability and adoption of mobile devices with sensors have significantly expanded the potential availability and volume of real-world data and evidence because of the continuous, passive, and objective nature of sensor-generated real-world data. These new kinds of data collection represent an opportunity for significant value in better measuring factors relevant to physical health, mental health, and care services. A particular kind of this sensor-generated real-world data that has value for the mental health space is continuous behavioral health measurement (CBHM). 

What is Continuous Behavioral Health Measurement and How Does it Work? 

Continuous behavioral health measurement is a means of using smartphone sensors to passively track behaviors that give insight into an individual’s mental states. It may even reveal factors that the individual may not always be consciously aware of. This type of measurement can be an effective addition to clinical surveys and validated scales such as the PHQ-9 and GAD-7, which are common measurements used as real-world data in many behavioral health management settings. 

More specifically, continuous behavioral health measurement works by collecting real-world information that smartphone sensors are already gathering on a continuous basis, such as: 

• mobility information 
• data on physical movement 
• typed language  

With the explicit permission of the data-producing individual, this sensor data can then be processed into feature data related to sleep, patterns of language and communication, likely physical activity, and potential correlation to positive and negative affect, and variations in mood. 

A Real-World Example 

A real-world example of continuous behavioral health measurement could be looking at factors relevant to a patient at risk for severe depressive or manic symptoms. These factors may include a combination of: 

• the individual not leaving the house for several days 
• sleeping less than 4 hours 
• typing and texting using patterns of language shown in research to indicate risk for suicide
   

Another example might be the case of a schizophrenia drug trial where continuous behavioral measurement can confirm how the drug is impacting not only positive symptoms like delusions and hallucinations, but also negative symptoms like social withdrawal, difficulty sticking with plans, and decreased motivation. Current trials that only use surveys and scales can only indicate outcomes that are reported by the trial participants. Without continuous behavioral measurement these trials miss behaviors related to several negative symptoms of schizophrenia which may give deeper insight into the drug’s effects across the full range of symptoms and functioning. This deeper insight results in richer real-world evidence and may even reduce the time to study endpoints. 

Such factors can be gathered and analyzed in near real-time using continuous behavioral health measurement of smartphone use.

Private Data: Value, Privacy, and Consent 

After installing a continuous behavioral health measurement platform on their phone, the individual may choose to access their information themselves to better support changes they may want to make in their life. 

In a health care context, they may choose to share their confidential data securely with their doctor or care providers by giving explicit permission to access their confidential data. 

In the case of a research study, they may consent to share their information with investigators studying human behavior. 

Better Insight, Care, and Interventions 

When this data is shared, it can result in better insights that can lead to support and better care. It also can supply richer real-world data that can improve our understanding of effective therapeutic and interventional combinations over time and in real-world settings, outside of controlled labs.  

Beyond Behavioral Health 

The real-world application of continuous behavioral health measurement has benefits not only for mental and behavioral health situations, but also for chronic illness situations where co-morbid depression or anxiety are often complicating factors. Such applications range from measuring endpoints in real-world clinical studies endpoint measurement to Risk Evaluation and Mitigation Strategy (REMS) drug safety programs. 

Filling the Gaps with Continuous Behavioral Health Measurement 

Continuous behavioral health measurement has real potential for filling reporting gaps often left by human limitations like inattention, distraction, and reporting fatigue brought on by often overburdened care providers. It is not designed to replace providers or standard clinical surveys and validated scales, but to enhance reporting with the kind of real-world data and evidence that can bring about better care and better outcomes. 

The gaps in measurement no longer need to be missing for patients, researchers, health and social care providers, and directors. Ksana Health has developed real-world data solutions based on years of NIH-funded academic research in mobile sensing and digital phenotyping and is making those solutions more available to patients and health care providers.  

The closing of these gaps can allow for the next level of research precision, preventive care, and innovative interventions. 

Request a Demo 

Please reach to Ksana Health for a demonstration if you are interested in learning more about how continuous behavioral health measurement can transform your real-world data and evidence programs.