Winston-Salem, NC – Clinical ink, a global life science technology company, now offers continuous glucose monitoring (CGM) within its integrated digital biomarker solution. The new modules enable remote collection, central storage, and analysis of sensor data from commercially available devices for use in diabetes clinical trials. The solution addresses device connectivity status, providing robust, on-device CGM data collection in both offline and online scenarios with efficient data synchronization management when connected. 

This integration is facilitated by the recent FDA clearance of commercial CGM devices, which include the Abbott FreeStyle Libre 3 System and the Dexcom G7 System. CGM is now also recommended in the expanded 2022 American Diabetes Association clinical guidelines for adults and children, as well as the 2022 FDA guidance on feasibility of use in diabetes clinical trials.  

“I am delighted that Clinical ink is leading the industry in the introduction of CGM as the latest addition to a comprehensive suite of digital biomarkers covering multiple therapeutic areas,” comments Jonathan Goldman, MD, CEO at Clinical ink., “These technologies have the potential to improve patient safety by increasing the frequency and resolution of real-time monitoring while decreasing clinical trial costs and time-to-decision.”

The Clinical ink data integration provides full centralized access to a suite of metrics that are compliant with CGM guidelines, along with behavioral, cognitive, and biometric measures. The ability to monitor the relationships between glucose levels, behavior, and physiological response in near real time provides unprecedented insights for actionable intervention and behavior modification. The solution completes the Clinical ink suite, which includes eCOA, decentralized clinical trial capabilities, and data science solutions that deliver complex diabetes protocols in the clinical environment.

“This work promises to expand Clinical ink’s data collection capabilities for diabetes clinical trials,” adds Stephen Polyak, PhD, VP of Engineering and Data at Clinical ink. “We believe CGMs are a valuable tool for clinical trials that will improve the quality and accuracy of glucose data, reduce participant burden, improve safety monitoring, and provide valuable longitudinal data.”

The addition of CGM capabilities further demonstrates the company’s commitment to providing innovative eClinical solutions for the pharmaceutical, biotechnology, and medical device industries. With this integration, Clinical ink solidifies its position as a leader in the industry, delivering advanced solutions for complex diabetes protocol design and deployment.

About Clinical ink
Clinical ink is the global life science company that brings data, technology, and patient-centric research together. Our deep therapeutic-area expertise, coupled with Direct Data Capture, eCOA, eConsent, telehealth, neurocognitive testing, and digital biomarkers advancements, drive the industry standard for data precision and usher in a new generation of clinical trials.   

The post Clinical ink Announces New Continuous Glucose Monitoring Solution appeared first on Clinical ink.

Digital biomarkers are biological or behavioral measures collected using digital devices or technologies such as smartphones, wearable devices, and sensors. Analyzing data collected through our collaborative Watch-PD study, we demonstrated digital biomarkers could detect both the presence of Parkinson’s disease (PD) as well as response to treatment. They also have the potential to transform the way we screen patients, evaluate treatment efficacy, and determine the best course of treatment for a given patient.  These biomarkers are especially promising in devastating central nervous system diseases like Parkinson’s and ALS where assessments are costly, invasive, and subjective.

How is this data used?

Clinical ink BrainBaseline was used to provide preliminary support to distinguish healthy volunteers from patients with early-stage PD and to generate digital biomarkers with PD status.

When the same dataset from the Watch-PD study was analyzed to determine if digital biomarkers could accurately assess symptomatic treatment response in PD, it also highlighted an important aspect of disease management, as it allowed us to evaluate the effectiveness of different treatments and adjust them accordingly. The results showed that digital biomarkers could indeed accurately assess treatment response in PD patients.

The study also explored the potential of digital biomarkers in predicting disease severity and progression in ALS patients. ALS is a devastating disease that can progress rapidly, making it difficult to predict outcomes and develop effective treatment plans. Digital biomarkers also indicated that valuable insights into disease progression could be discovered to help clinicians develop more personalized treatment plans.

What are the implications for clinical development?

Early indication of efficacy.
Using the Clinical ink assessments and BrainBaseline, it is possible to quickly know if a drug has the desired effect on patient symptoms/pathophysiology. 

Reduced patient burden.
The rapidity of measures could potentially reduce patient burden by upwards of 50 percent.

Lower development costs.
Clinical studies using digital biomarkers can potentially be designed to have ample statistical power with fewer patients than required. Insights from digital measures could also be used to identify patient phenotypes within a disease.

Decreased site involvement.
It is possible that eventually digital biomarkers could be accepted by regulators as primary endpoints.

The digital biomarkers collected from active assessments performed on the Apple Watch and iPhone, then analyzed through our BrainBaseline platform, were able to:

• Register the presence of early-stage PD, successfully distinguishing healthy volunteers from PD patients 
• Detect treatment response in PD 
• Identify measures indicative of disease progression in ALS patients

Having demonstrated that the data features gathered from digital devices and the models employed can detect treatment response in PD and measure disease progression in ALS, we believe it is possible to apply the same approach to other movement disorders. 

The results of our work with data derived from digital assessments performed by PD and ALS patients have demonstrated that consumer-grade mobile devices—coupled with the BrainBaseline platform—can produce digital biomarkers that have significant value in clinical development and eventually in clinical practice. 

Download this white paper for a deep dive into the analysis of the impact of digital biomarkers in movement disorders.

The post Digital Biomarkers in Movement Disorders appeared first on Clinical ink.

In late 2022, DPHARM (Disruptive Innovations to Modernize Clinical Research) sat down with Joan Severson, our Chief Innovation Officer, to discuss how sensor fusion expedites clinical research, and how to foster innovation in our industry by the expert design and deployment of digital tools.

Read the interview below as originally published by DHARM.

What work is Clinical ink leading to disrupt and innovate clinical research?

My team is immediately focused on taking the data from mobile, sensors, and wearable technology and fusing it together to create a holistic picture of the patient. We do this by integrating data from the everyday context of a patient’s life – what we call “patient science” – and fusing it with more traditional clinical data. We’re at the beginning of something that will drastically change the industry. By fusing these wearable, sensor, and mobile data together, we are able to create very rich data models to better prove or more quickly disprove therapy efficacy – as well as understand an intervention’s effect on a patient’s quality of life. As we look to the future, my team is most excited about our work in tying passive data to active assessments, as well as possibilities that open up when working with patient-consented geofencing and GPS data integration. 

What is the “patient science” data you’re referring to?

At Clinical ink, our vision is to advance clinical discovery by positioning ourselves at the convergence of data, technology, and patient science. Everyone in our industry uses phrases like patient-centered or patient centricity, but we believe the future of clinical trials is truly dependent on powering patients to become more active participants in science. We have an obligation to develop comprehensive, actionable, patient-centered assessments and measurements that put science back into the patient’s hands – or, as I like to say, on their wrists and in their pockets.

How are you addressing the challenges of integrating data from a patient’s life with clinical-grade data?

We’ve developed processes and platforms that are designed to support the evolving modalities of patient data capture both in the clinic and at home; voice, movement, lifespace, cognition, mood, activity, medication use, biometrics. Our platform supports ingestion of data from our native applications in clinic and at home, as well as third party sources. As much as we would like to see everyone’s data formatted for ingestion – processing and synchronization through industry data standards and APIs – the reality is we will probably always have cases where we need to do some analysis to bring data together and to create data models that support study operations as well as data analysis processes and pipelines.

How do you give an accurately weighted context to something gathered from a patient’s everyday life and put it into the scale used for clinical data?

It’s not necessarily about the weighting of data, it’s about fusing this incredibly rich data together to create an enhanced model of a patient’s condition and its effect on their quality of life. Our data science team works with patients, study sponsors, clinical teams, and our research collaborators to understand what impacts patients’ quality of life and how. At the end of the day the platform needs to build a model of a patient quite similar to how a physician builds their own model in their chart each time they are presented with a patient. We are not intending on replacing that physician, but we are building an additional model of the patient that lives in digital space.

Can you give an example of sensor fusion?

We’re doing this work every day. You can see this play out brilliantly in our public WATCH-PD work in collaboration with the University of Rochester, Biogen, Takeda, and the Critical Path Institute. In this study – participants used an iPhone and Apple Watch to collect active tasks and passive behavioral data for over 12 months. Tasks included active psychomotor, cognitive, voice tasks and ePROs (mood, fatigue, ADLs), and continuous passive behavioral data streams. These assessments were all aligned with the Unified Parkinson’s Disease Rating Scale (UPDRS). The interdisciplinary work of this study will aid in developing digital biomarkers to better understand Parkinson’s patient burden, so that the industry can target therapies and address quality of life.

How has your advanced technologies and digital biomarkers team deployed sensors and mobile devices for sensor fusion?

The team has been deploying sensors and mobile devices into clinical studies for nearly a decade, with the end goal of creating digital endpoints, or biomarkers. We have never been closer. We’ve partnered with some of the most respected pharmaceutical companies, research institutions, and government organizations to deploy the patient use of consumer-grade devices for research of various indications – Parkinson’s and movement disorders, Oncology, COVID and other respiratory conditions, as well as rare diseases. We are currently conducting studies that tie mobile cognitive assessments with mobile voice capture to better understand and create digital biomarkers for long COVID. We’re excited to share more about this work.

How is sensor fusion technology enabling a better understanding of disease progression?

By coupling sensor fusion technology with disease-focused feature engineering and artificial intelligence, we are able to demonstrate that more frequently acquired, remotely-monitored measures yield greater sensitivity to disease progression. We do this by capturing high temporal resolution sensor data via collection of both active mobile assessments – measuring mobility, cognition, and voice – and passive mobility data. Using our source of abundant multivariate data, we have extracted data features selective for disease status. We then feed those features into a random forest model and evaluate its classification accuracy in predicting disease status in an independent dataset. In the WATCH-PD study, our model was able to distinguish the healthy volunteers from early-stage PD patients with 92% accuracy, providing preliminary support for the use of our platform in generating digital biomarkers of early-stage PD status.

How do you foster an innovative environment at Clinical ink and amongst your team?

By championing an interdisciplinary team with wide ranging experiences, perspectives, and expertise. Every day, we apply principles from human factors, computer science, clinical practice, neuroscience, multivariate data analysis, user experience design – even aerospace engineering – in order to concept, develop, and deploy our technology. This creates an environment of constant problem solving and commitment to progress toward a common goal. You have such a long history with digital tools, such as wearables and other mobile technologies, in clinical trials.

What is your perspective on how they’re being used today in clinical research?

Sophisticated digital technologies are sorely underutilized in clinical research. Mobile sensors and wearable technology now afford us a promising class of more objective, performance-related research tools that complement eCOA and ePRO. We can now take advantage of ubiquitous, patient-friendly mobile and wearable technologies. We need to do it more and tap into the power of responsive user experience design that can support patients to enable more diverse participation, create better engagement, reduce anxiety, and build trust.

What is your chief goal or north star as Clinical ink’s Chief Innovation Officer?

To provide robust solutions in the sensors, wearables, and digital biomarkers space. And my standard for that is very high. This is not about throwing a wearable into a trial and hoping for the best; it’s about ensuring we offer the translational, applied research expertise to guarantee success and spark true innovation.

Learn more about sensor fusion, Clinical ink sensor engineering expertise, and the need for powerful, collaborative analytics platforms to develop disease-specific digital biomarkers.

The post An Interview with Chief Innovation Officer Joan Severson: How Sensor Fusion Expedites Clinical Research appeared first on Clinical ink.

The Clinical ink Mobile Gait and Balance Assessment measures visuomotor coordination, balance, and motor performance.  It utilizes multiple sensors attached to participants to characterize biomechanical movements and identify abnormal walking patterns.^1,2

In this task, participants walk at a normal pace along a straight path, back and forth. Walking patterns are recorded from the trunk and wrist.

With the data collected, we can analyze and calculate metrics associated with gait speed, efficiency, and synchronicity, along with other disease-specific features.

Why Measure Gait and Balance?

Gait is sensitive to changes in visuomotor coordination, balance, and motor performance. Diseases affecting brain regions involved in the ability to efficiently maintain their walking pattern, volitionally control their limbs, or safely recognize and avoid hazards in their environment can negatively affect Gait performance. For example, altered Gait performance has been demonstrated in Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis. ^3-5  Using Gait measurements, disease- and treatment-related changes in visuomotor coordination, balance, and motor performance can be estimated.

Discover the Additional Benefits of Clinical ink’s Technology

Learn more about how Clinical ink’s wearable and sensor technologies power the Mobile Gait Assessment, as well as other biomechanical, cognitive, and speech assessments here, or download the Clinical ink Big Brain Book, a digital assessments and endpoints catalog.

Sources:

1. Takeda, R et al (2009). Gait analysis using gravitational acceleration measured by wearable sensors. Journal of Biomechanics, 42, 223-233.

2.  Senden, R (2009). Acceleration-based gait test for healthy subjects: Reliability and reference data. Gait & Posture, 30, 192-196.

3. Latt, MD et al (2009). Acceleration patterns of the head and pelvis during gait in older people with Parkinson’s disease: A comparison of fallers and nonfallers. Journal of Gerontology, 64A, 700-706.

4. Spain, RI et al (2012). Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait & Posture, 35, 573-578.

5. Hausdorff, JM et al (2000). Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. Journal of Applied Physiology, 88, 2045-2053.

The post Clinical ink Mobile Gait Assessment appeared first on Clinical ink.

The Clinical ink Mobile Postural Tremor Assessment measures arm stability and motor performance by measuring moment-to-moment changes in upper limb position and velocity.

Data is captured by mobile phone and smartwatch. During a baseline rest phase, participants rest their hands in their lap. During an active posture phase, participants hold their arms out, palms facing down.

Through signal processing of different frequencies, we can measure changes in upper limb position and velocity from  diseases affecting motor control as well as treatment related changes.

Why Measure Postural Tremor?

Voluntarily attempting to maintain a position against gravity is sensitive to changes in motor performance. Diseases affecting brain regions involved in the ability to maintain steady posture and arm position can negatively affect Postural Tremor performance. For example, altered performance has been demonstrated in Parkinson’s disease, multiple sclerosis, and stroke.^1-3  Using Postural Tremor measurements, disease- and treatment-related changes in motor performance can be estimated.

The Role of Clinical ink’s Technology

Learn more about how Clinical Ink’s sensors and wearables technology power the Mobile Postural Tremor Assessment and its data collection, as well as other mobility, cognitive, and speech assessments, or download the Clinical ink Big Brain Book, a digital assessments and endpoints catalog.

Sources:

1. Lance, JW, Schwab, RS, & Peterson, EA (1963). Action tremor and the cogwheel phenomenon in Parkinson’s disease.Brain, 86, 95-110.

2.  Koch, M et al (2007). Tremor in multiple sclerosis. Journal of Neurology, 254, 133-145.

3. Bansil, S (2012). Movement disorders after stroke in adults: A review. Tremor and Other Hyperkinetic Movements, 2,tre-02-42-195-1.

The post Clinical ink Mobile Postural Tremor Assessment appeared first on Clinical ink.

The Clinical ink Mobile Finger Tapping Test measures a patient’s ability to rapidly generate and coordinate rhythmic motor movements.

Participants place their index and middle fingers on the circles and rapidly tap them in alternating order. Taps completed correctly add to the “Total Taps” counter during a  dominant hand and non-dominant hand test. Performing coordinated movements requires sufficient motor control to make them consistent and repeatable.

Why Measure Finger Tapping?

Finger Tapping is sensitive to changes in visuomotor coordination and motor performance. Diseases affecting brain regions involved in the ability to generate coordinated motor movements can negatively affect Finger Tapping performance. For example, altered Finger Tapping performance has been demonstrated in Parkinson’s disease, schizophrenia, and alcoholism.^1-3 Using Finger Tapping measurements, disease- and treatment-related changes in visuomotor coordination and motor performance can be estimated.

Learn About the Benefits of Clinical ink’s Technology

Learn more about how Clinical ink’s sensors and wearables technology powers the Mobile Finger Tapping Test, as well as other mobility, cognitive, and speech assessments, or download the Clinical ink Big Brain Book, a digital assessments and endpoints catalog.

Sources:

1. Tavares, ALT et al (2005). Quantitative measurements of alternating finger tapping in Parkinson’s disease correlate with UPDRS motor disability and reveal the improvement in fine motor control from medication and deep brain stimulation. Movement Disorders, 20, 1286-1298.

2.  Carroll, CA et al (2009). Timing dysfunction in schizophrenia as measured by a repetitive finger tapping task. Brain and Cognition, 71, 345-353.

3. Parks, MH et al (2006). Brain fMRI activation associated with self-paced finger tapping in chronic alcohol-dependent patients. Alcoholism: Clinical and Experimental Research, 27, 704-711.

The post Clinical ink Mobile Finger Tapping Test appeared first on Clinical ink.

The Clinical ink Mobile Phonation Assessment measures emotional affect, voice and respiratory function.

Data from the phonation assessment is captured by phone. Participants are instructed to speak syllables such as “ahh” as long and as loud as possible within a single breath.

Why Measure Phonation?

Phonation is sensitive to changes in emotional affect, voice, and respiratory function. Diseases affecting regions of the central nervous system involved in the ability to produce or maintain phonations over an extended period of time can negatively affect Phonation performance. For example, altered Phonation performance has been demonstrated in Parkinson’s disease, amyotrophic lateral sclerosis, multiple sclerosis, and respiratory disease.^1-4 Using Phonation measurements, disease- and treatment-related changes in emotional affect, voice, and respiratory function can be estimated

Discover the Additional Advantages of Clinical ink’s Technology

Learn more about the Clinical ink’s sensors and wearables technology powers Mobile Phonation Assessments, as well as other mobility, cognitive, and speech assessments here, or download the Clinical ink Big Brain Book, a digital assessments and endpoints catalog.

Sources:

1. Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2010). Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. Journal of The Royal Society Interface, 8, 842–855.

2.  Ramig, LR et al (1988). Acoustic analysis of voices of patients with neurologic disease: Rationale and preliminary data. Annals of Otology, Rhinology, & Laryngology, 97, 164-172.

3. Nordio, S et al (2018). Expiratory and phonation times as measures of disease severity in patients with multiple sclerosis: a case-control study. Multiple Sclerosis and Related Disorders, 23, 27-32.

4. Tong, JY & Sataloff, RT (2020). Respiratory function and voice: The role of airflow measures. Journal of Voice.

The post Clinical ink Mobile Phonation Assessment appeared first on Clinical ink.

Horsham, PA – Clinical ink, a global life science technology company, has developed a self-testing app to measure cognitive impairment in patients diagnosed with COVID-19 compared to those without COVID-19. The app is currently deployed in a study of the risk factors, symptoms, disease course, and clinical outcomes of COVID-19 among members of the US military population.

The Uniformed Service University (USU) Infectious Disease Clinical Research Program (IDCRP) is conducting the “Epidemiology, Immunity, and Clinical Characteristics of Emerging Infections Diseases with Pandemic Potential,” or EPICC Study to further the IDCRP mission of reducing the impact of infectious disease in the military population. Study results have and will be further shared with Military Health System leaders and others with the goal of informing clinical care and practice guidelines for managing patients with COVID-19. IDCRP is supported by The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF).

As neurocognitive complications, e.g. ‘brain fog,’ have frequently been reported with COVID-19 and present a significant concern for active duty personnel and others, IDCRP researchers sought a way to evaluate the presence, severity, and domains of cognitive impairment among study subjects. Clinical ink’s mobile app, which is built on the BrainBaseline platform—a scientifically validated research solution—is designed to do that using measures that digitally replicate those used in large, in-clinic cohort studies.

“Because the self-testing platform works on participant’s electronic devices—their personal smartphones as well as tablets—and is easy to use, it collects cognitive data remotely, without the need for specially-trained testers,” said Joan Severson, Chief Innovation Officer at Clinical ink.

The mobile assessments require no more than 20 minutes of the participant’s time, and are specifically designed to test the user’s memory, attention, cognitive flexibility, visual short-term memory, and processing speed. Designed by Clinical ink’s internal engineering, clinical, and user experience (UX) experts, the app also includes embedded information about the study and easy-to-understand instructions for completing the assessments. It is currently available for download within the Apple App Store only for study participants

“HJF appreciates how enabling technology can improve research processes, efficiency, and outcomes,” said Dr. Joseph Caravalho, President and Chief Executive Officer of HJF. “Deploying such technology in clinical trials with this select study population has the added long-term benefit of identifying best practices for its use in the general population.”

Of the approximately 7,500 participants currently enrolled in the EPICC study, up to 1,000 will be asked to complete the mobile, cognitive self-assessment as part of a sub-study module at a single cross-sectional timepoint, and again in 6 months for those that are still active in the study at that time.

“We’re delighted to possess the advanced capabilities to furnish USU with a tool to complete this invaluable research,” continued Ed Seguine, Chief Executive Officer at Clinical ink. “The need to better understand the long-term effects of COVID-19 on cognition is one of the most pressing research needs of our current time. We’re proud to be adding to the scientific record in this important area.”

More information about the study can be found here.

About Clinical ink

Clinical ink is the global life science company that brings data, technology, and patient science together. Our deep therapeutic-area expertise, coupled with Direct Data Capture, eCOA, eConsent, telehealth, neurocognitive testing, and digital biomarkers advancements, drive the industry standard for data precision and usher in a new generation of clinical trials. By harnessing digital data, we power sponsors, CROs, researchers, and patients to recenter decentralized trials and rewrite the clinical development experience.

The post The US Military Health System Research Study Deploys the Clinical ink Self-Testing Mobile Platform to Understand Long Covid appeared first on Clinical ink.

DHTs generate high-dimensional data sources instrumental to classifying behavioral and physiological states, including actigraphy, heart rate, sleep, glucose levels, speech, and cognition. Leaders in this industry have engineered numerous pipelines to integrate DHTs and deliver quality-assured data directly from participants to customers. For newer players in this space, there are a multitude of questions and complexities to be considered before providing Sponsors and researchers with the optimal solutions for their clinical trial needs.

From enrollment to insight, engineering pipelines ensure the highest quality data standards. Operational standards must be considered for promoting patient engagement in data collection, identifying appropriate technologies to capture relevant signals, securely transmitting data from devices to servers and customers, developing a scalable clinical analytics platform, conducting technical and clinical validation studies, and developing digital biomarkers. 

In this blog, we provide a brief glimpse into how Clinical ink is developing and employing a robust clinical analytics platform. When properly executed, an analytics platform generates meaningful and insightful feedback loops that permeate throughout and improves upon the engineering pipeline, allowing our team and partners to understand and trust their highly complex data at an unprecedented level.

The Clinical ink Advanced Technology & Analytics team has developed a robust, scalable analytics platform to support internal development and customer insights. Our multidisciplinary team of scientists, architects, data engineers, data scientists, and software engineers are using a highly collaborative analytics platform to drive innovation forward. Together, we have achieved success in implementing solutions throughout the engineering pipeline, including:

• Applying methods to improve patient engagement and compliance
• Performing root cause analyses to understand patient behavior in bring your own device (BYOD) studies
• Evaluating sensor data streams to identify optimal solutions for capturing disease-relevant signals
• Fusing data sources from multiple sensors into a common reference frame
• Ingesting data into enterprise data lake solutions for internal research and external customer delivery
• Visualizing data-monitoring dashboards that track patient adherence and data quality
• Real-time quality control checks on incoming data sources
• Utilizing best-in-practice data science notebooks to support collaboration and reproducibility
• Constructing multivariate modeling methods to evaluate mixed effects in complex study designs
• Engineering features using time- and frequency-dependent signal processing routines and algorithms
• Building machine learning solutions to improve platform performance and patient-centric insights
• Validating measurements derived from sensors, algorithms, and clinical data
• Developing disease-specific digital biomarkers

The future growth of Sponsors and researchers incorporating or planning to incorporate DHTs into their clinical trials will depend on ensuring the highest standards of data collection, transmission, security, quality, and analysis. Scalable clinical analytics platforms are essential to implementing and reinforcing these data standards. Building out a comprehensive analytics platform, as enumerated above, within core engineering frameworks ensures the integrity, repeatability, and value of data standards and insights.

Additionally, developing an analytics platform within the core engineering framework demands a multidisciplinary team of engineers and scientists. At Clinical ink, we have innovatively brought these key pieces together to create an analytics solution that promises to take clinical trials incorporating Digital Health Technologies (DHTs) to the next level.

Discover more with Clinical ink: 

Want to understand the basics of sensor fusion in clinical trials? See our previous blog post. 

Curious about how our team applies UX design principles to develop and deploy DHTs? Learn more in our previous blog post.

Author

David Anderson, Ph.D.
Principal Scientist, Clinical ink

The post Integrating Sensors into Clinical Trials Demands a Powerful Analytics Platform appeared first on Clinical ink.

Years ago, the US Bureau of Product Safety within the Food and Drug Administration (FDA) required that an ad demonstrating the flexibility of a razor blade include the warning: “Don’t try this at home.” These are wise words that can also be applied to sensor fusion, or fusing together biometric data from multiple consumer-grade sensors and wearables, along with a variety of other data sources. The fused data reveal rich details about a patient’s condition and experience and can be used as secondary endpoints in drug development.

The process of developing or adapting sensor/wearable applications to collect digital biomarkers is a very involved – and highly specialized – one that requires a mix of engineering, computer science, and, of course, clinical knowledge. Ensuring success in such an effort requires:

• An interdisciplinary team. Collectively, those working to fuse multiple real-world data inputs must understand digital technologies, data analysis methodologies, and clinical trial processes. As a start, they must be familiar with the different device operating systems and applications and what they are capable of capturing. They must also be experts in designing user experiences, in cognitive psychology, and signal processing. At the same time, the team must know how the data will be used to support drug development – which means understanding the characteristics of the particular disease and the relevant ways that disease progression and disease burden are measured. This is all in addition to needing to know how to work with all the moving pieces of a clinical trial, which includes satisfying the information needs of Institutional Review Boards (IRBs) and regulators.

• Data science expertise across modalities. The data collected from different types of digital devices are so unalike that they require the expertise of very specialized data scientists. For instance, analyzing voice data requires different expertise than analyzing movement data, and it is unlikely that the same data scientist could do both.

• A flexible data analysis platform. The technology must be able to ingest diverse types of data (including from third-party applications) and support multivariate analyses, modeling, and simulation through machine learning and artificial intelligence.

The goal – meeting data capture and reporting needs – by fusing digital biomarker data with other data may sound standard enough for the industry, but in actuality it is quite a new field that few have mastered to date. And, knowing how to collect and work with data from one type of device doesn’t translate into experience that can apply to any other type of device, let alone to fusing different data sets. Those who “try it at home” so to speak and attempt to implement sensors and wearables without a fusion strategy and the necessary experience and expertise are unlikely to be successful.

Want to understand the basics of sensor fusion? See our previous blog post.

Learn more about Sensor Fusion in the White Paper “Digital Biomarkers as Endpoints in Parkinson’s Disease”

See a real-world example of Sensor Fusion in action in our WATCH-PD Case Study.

The post How Clinical ink Fuses Sensor Data appeared first on Clinical ink.

Today, we can get closer to the “truth” of a patient’s condition and experience by synthesizing different real-world measures of disease pathology and treatment response through sensor fusion. It’s more possible than ever to fuse together biometric data from multiple consumer-grade sensors and wearables – along with a variety of other data sources, such as patient reported outcomes (PROs). The fused data can serve as a robust source of evidence that can be used as secondary endpoints in drug development. Ultimately, this more holistic view will help deliver better and more cost-effective health care and disease prevention.

Richer Context for Data Interpretation

When patients present for treatment at the clinic, physicians are trained to combine the results of objective assessments with their general observations to form a holistic picture of the patient’s health status. In much the same way, by fusing together data from multiple real-world assessments, we can gain a more complete – indeed more accurate – picture of the patient. With the right tools and expertise, we can fuse disparate forms of data from different digital modalities and then develop models to understand the pathology of the disease under study. The resulting combined view, while not in any way replacing the physician’s clinical assessment, moves us closer to painting a picture of the whole patient. Plus, it comes from the real-world setting.

Perhaps it’s easiest to appreciate the value of looking at digital biomarkers in combination and comprehensively (as opposed to individually) by sharing a few examples of the capabilities:

• Voice data can be combined with data from patient diaries in a study of respiratory therapy. If a patient reports having difficulty breathing, data from voice-assessments gathered via the patient’s smartphone can not only corroborate that report, but can also provide a much more detailed understanding of the nature of the difficulty. We can measure no less than 400 different features from a voice recording file to assess everything from phonation to lung acoustics.
• For a study of a movement disorder, data collected passively from a smartwatch can be combined with data collected from the patient’s active completion of active movement tasks on a smartphone. When combined, the data presents a more complete understanding of the patient’s movement that includes arm swing, gait, and movement of the torso.
• A study assessing the quality of life (QoL) of a senior population might fuse data gathered on patients’ mobility from GPS or Geographics Information Systems (GISs) with data on social determinants of health to understand a patient’s lived experience and ability (or non-ability) to interact with the world outside.

Combining data sources in this way provides a richer context for data interpretation and, because the digital data can be collected in real time, it provides full situational awareness in a clinical study.

An Innovative, Multi-Disciplinary Capability

All this is not to say that fusing data from multiple digital sources is easy to do. Indeed, few can do it, and the concept is relatively new in the life sciences industry. That’s one reason why Clinical ink has found it helpful to hire experts from various sectors, including the aerospace and automotive industries. Without the right experience, sponsors can find that they’ve collected data that isn’t usable or that the data analysis fails to provide the type of insights needed.

At Clinical ink, we consider the entire process—from consulting on a protocol to selecting the device and developing the user interface, to data modeling and analysis—a form of engineering. (Engineering is not, of course, a field normally associated with the pharmaceutical industry). The engineer’s goal is to transform digital biomarkers into measures that are clinically relevant.

Conclusion

The field of digital biomarkers is advancing very quickly, and fusing data collected from different digital sources is becoming a best practice in clinical development, and this is possible thanks to sensor fusion. Companies that are including digital biomarkers as exploratory endpoints in their research today will have a distinct advantage in speeding their products to market

Learn more about Sensor Fusion in the White Paper “Digital Biomarkers as Endpoints in Parkinson’s Disease”

See a real-world example of Sensor Fusion in action in our WATCH-PD Case Study

Author

Joan Severson
Chief Innovation Officer,
Clinical ink

The post Sensor Fusion Reveals a Comprehensive Picture of the Patient appeared first on Clinical ink.

Wearable and sensor technologies empower researchers to capture the full spectrum of patient health—formerly inaccessible, but now available through the computing technology of both clinical-grade as well as consumer-grade electronics.

Listen to Clinical ink Principal Scientist David Anderson review the e-Poster “WATCH-PD: Detecting Early-Stage Parkinson’s Disease Status Using Feature Engineering and Machine Learning in Sensor-Based Assessments.” The work is part of the International Congress of Parkinson’s Disease and Movement Disorders program, held in Madrid, Spain from September 15-18, 2022.

Walk through the research with Anderson, and learn how feature engineering and machine learning modeling allowed the team to accurately predict early Parkinson’s Disease (PD) status with 92.3% accuracy, 90% sensitivity, and 100% specificity, across environmental and temporal contexts.

You’ll learn more about:

Author

David Anderson, Ph.D.
Principal Scientist,
Clinical ink

The post Deploying Feature Engineering and Machine Learning in Sensor-Based Assessments appeared first on Clinical ink.