Merck, Evidation join forces on Alzheimer’s data solutions

By Jenni Spinner

- Last updated on GMT

(wildpixel/iStock via Getty Images Plus)
(wildpixel/iStock via Getty Images Plus)

Related tags Merck Alzheimer's disease Data collection Remote patient monitoring Medical devices

The health data specialist and pharma firm are partnering on solutions around remote collection of digital data for detection and monitoring of treatments.

Evidation and Merck (known as MSD outside the US and Canada) have announced a collaboration geared toward investigating the usefulness of remotely collected digital measures, in order to detect and monitor Alzheimer’s treatment. According to the partners, the work will center on the identification of Alzheimer’s digital endpoints that can be collected remotely to accelerate drug development.

Outsourcing-Pharma discussed the project and what it might mean for the future of Alzheimer’s detection and treatment with Deb Kilpatrick, co-CEO of Evidation.

OSP Could you please share an ‘elevator presentation’ description of Evidation—who you are, who you serve, key capabilities, and what sets you apart from the competition?

DK: Evidation measures health in everyday life and enables anyone to participate in groundbreaking research and health programs. We partner with leading healthcare companies to better understand health and disease outside the clinic.

Over 4m individuals use our Achievement app, which is open to all adults in the US. Through Achievement, also accessible on a browser, individuals can connect smartphones, wearables, and connected devices that generate heart rate, activity, sleep quality, and other health-related data; they can connect health apps like Strava and MapMyFitness, and they can participate in surveys and provide patient-reported outcomes (PROs) of many forms. Achievement members are rewarded for taking these regular health actions and participating in research, a long-held and common practice in traditional medical research.

Evidation is the only organization of its kind that puts individuals at the center of its mission in its quest to understand health outside the clinic walls, all with a focus on trust and privacy. Achievement represents all 50 states and nine out of every 10 US ZIP codes. Evidation delivers health programs across a broad array of therapeutic areas and works with nine of the top 10 global biopharma companies, in addition to other leading healthcare organizations. 

OSP: How did you come to collaborate with Merck—have you worked together before?

DK: Merck is looking to develop digital measures used to distinguish individuals with and without cognitive impairment and determine if these digital endpoints can be used to track changes over time that may signal diseases like Alzheimer’s. Being able to more quickly and accurately distinguish these individuals will accelerate drug development.

Evidation is a leader in leveraging Person Generated Health Data (PGHD) to advance healthcare. With privacy and per-use consent at the center of Evidation’s work, Evidation’s diverse and engaged research population offers Merck new ways to understand PGHD and develop measures to improve the identification of Alzheimer’s disease earlier.

OSP: Please talk about how research into Alzheimer’s and potential therapies has evolved in recent years.

Deb Kilpatrick, co-CEO, Evidation

DK: The data generated from commercial wearables in recent years is transforming how we think about and conduct research on cognitive impairment. Through these technologies, we’re able to gain insights into how people’s everyday behaviors change over time and how those changes are associated with cognitive diseases like Alzheimer’s.

Studying cognitive decline and developing early-acting therapies has been challenging because the progression of symptoms can be very slow and existing methods of measuring cognitive decline are often expensive, burdensome to patients, and infrequently administered. In contrast, consumer wearables and the data they generate can capture daily functioning, reduce patient burden, reach a wider audience, and potentially offer earlier or faster indicators of cognitive decline than traditional methods.

OSP: You mention your prior neurodegenerative research using data culled from digital sensors and apps; could you please tell us what you can about that work, and provide more detail about how this partnership will build upon that?

DK: In 2019, Evidation partnered with Eli Lilly and Apple to study how connected devices could be used to differentiate those with mild cognitive impairment from those with mild Alzheimer’s disease dementia. This study with Merck builds on the idea that sensor data can be used to better identify diseases, accelerate diagnoses, and target interventions.

OSP: Is there anything you’d like to add not touched upon above, or in your announcement?

DK: Clinical recognition of Alzheimer’s disease can often lag behind other indicators like changes in cognition and motor function. By studying these indicators, made available by the vast amounts of behavior data produced by connected sensors every day, we can open the door to earlier diagnoses and better interventions. 

Related news

Show more

Related products

show more

Saama accelerates data review processes

Saama accelerates data review processes

Content provided by Saama | 25-Mar-2024 | Infographic

In this new infographic, learn how Saama accelerates data review processes. Only Saama has AI/ML models trained for life sciences on over 300 million data...

More Data, More Insights, More Progress

More Data, More Insights, More Progress

Content provided by Saama | 04-Mar-2024 | Case Study

The sponsor’s clinical development team needed a flexible solution to quickly visualize patient and site data in a single location

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Related suppliers

Follow us


View more