Horsham, PA – Clinical ink, a global life science technology company, announces the publication of “Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study” in npj Parkinson’s Disease.

Baseline data from the WATCH-PD study—a longitudinal, multicenter study that deployed consumer-grade wearable and sensor devices loaded with a movement disorders application developed by Clinical ink—underwent feature engineering and statistical modeling to evaluate the technology’s sensitivity to symptoms of Parkinson’s disease. 

The application consisted of digital assessments of cognition, speech, and motor performance. Results demonstrated significant associations between the digital biomarkers developed from the wearable device and sensor data streams, and conventional clinical scoring methods used in Parkinson’s disease. 

“Parkinson’s disease is a neurodegenerative disease with increasing prevalence.  However, diagnosis of early Parkinson’s disease remains difficult due to complexity of symptoms,” said David Anderson, Ph.D., Principal Scientist at Clinical ink. “This publication demonstrates the potential use for consumer wearables in the detection and staging of early-stage Parkinson’s disease.” 

“We are thrilled to co-author this study in npj Parkinson’s Disease,” added Jonathan Goldman, MD, Chief Executive Officer of Clinical ink. “I am delighted that Clinical ink is taking a leadership role in clinical research applications of these novel technologies.  I hope that wearables and associated analytic tools can improve the lives of patients living with Parkinson’s disease and other movement disorders.”

The WATCH-PD study was funded with support from Biogen, Takeda, and the Critical Path for Parkinson’s Consortium 3DT.

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 Work Demonstrating Technology Sensitivity to Parkinson’s Disease Published in Nature appeared first on Clinical ink.

Sponsors considering using data from sensors and wearables in their clinical trials worry about three scenarios: collecting too little data, collecting too much data, and collecting bad data.

Each outcome could likely impact their study or a portion of it with a significant waste of time, money, and patient goodwill.

How can you de-risk the use of directly captured data from patients? How can you ensure that incoming data is properly ingested, stored, consumed, and managed to avoid potential issues?

Consider the specialized expertise required to integrate the right data collection application—a unified data platform—and the most advanced analytical methodologies.

Here is some insight from the Clinical ink Advanced Technology Team:

Solving “Too Little Data”

Sponsors may end up with too little data and insufficient statistical power when a protocol is too taxing for patients. Recruitment falters and adherence drops off. Getting expert consultation on protocol design provides an early and thorough assessment of all the abilities and limitations of the target patient profile.

Understanding patient insights and the use of patient-centered measures and assessments guides the technology strategy that will help prevent situations leading to “too little data.”

Clinical ink on Solving “Too Little Data”

In one assessment, recommendation against requiring prospective participants to complete a nearly 100-question survey at the start of the study could prevent loss of participation.

Evaluation concluded the process represented a burden that the particular patient population may not overcome.

Managing “Too Much Data”

Can you ever really have too much data? Maybe not, but wearables and sensors generate colossal and continuous streams of data. You can have more data than a particular system can handle or analysts can interpret.

The Clinical ink solution involves using a data platform that can store, process, and analyze any type and volume of data associated such as patient cognition, mobility, and phonation. Technology led by an expert analyst team converts incoming raw data into file formats — and ultimately dashboards — that Sponsors can easily consume (See Fig. 1).

Clinical ink on Managing “Too Much Data”

For instance, one assessment used in studies in Parkinson’s disease and COVID is a verbal phonation exercise in which the patient is asked to sustain a certain sound (such as “ah”). The incoming data is binary — a series of ones and zeros — and readable only by machines.

Converted into human-readable files that contain variables enables data scientists to screen important data for analysis. Getting reports and visualizations for data analysts to query the data is crucial in detecting signal patterns and drawing conclusions.

 

Preventing “Poor Quality Data”

Ensuring that the data collected from sensors and wearables is clean and usable requires a deep understanding of the therapeutic area, a detailed knowledge of industry standards, cutting-edge research analytical practices, and a patient-centered approach.

To ensure that collected data is of the highest integrity, here are few rules:

 

• • Rely on applications and tools that have been evaluated for that specific research purpose. In any therapy area, leverage industry standards and consult with experienced research leaders to determine what signals will be most meaningful.
     

• • Apply patient science, using qualitative and quantitative data collection methods, to ensure that the research approach is centered around the patient rather than the technology.
     

• • Design applications to create an excellent user experience that encourages adherence to the protocol and minimizes the opportunity for “user error.”
     

• • Clean and filter data, at the individual patient level, to maximize usability and efficacy for data analysis and modeling.
     

• • Consult your own quality control dashboards as data points come in and at the conclusion of studies to continuously improve methodologies.
     

Concerns over volume and quality of data generated in a trial are natural and should always be discussed as potential risks. Consider overcoming these challenges as fundamental to the success of any study — particularly those using eSource and more complex streams of patient data gathered from sensors and wearables.

To learn more about how Advanced Technology Teams in our industry operate, and how they prepare some of the most complex datasets for analysis, read “Debunking Complex Data Preparation and Analysis.”

Author

Anna Keil
Senior Software Engineer, Clinical ink

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The post The Three Perils of Wearable Data 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.

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.

Patient-friendly sensors and wearable technologies are now so ubiquitous that we can take advantage of them in clinical trials to measure voice, movement, life space, cognition, mood, activity, medication, use, and biometrics. Yet, it’s not as easy as throwing a wearable into a clinical trial and hoping for the best clinical user experience. Ensuring that the device supports diverse trial participation, creates better engagement, reduces user anxiety, and builds trust requires applying user experience (UX) design principles and practices to improve the patient experience in clinical trials.

How to Develop and Implement Good UX Design

Good UX design in clinical trial technology follows a carefully developed process that requires an interdisciplinary team with wide-ranging experiences, perspectives, and expertise. The figure below illustrates the stages of development that are involved in engineering wearable or sensor technology for a study. Note that attention to the user experience appears as a constant—from conceptual design all the way through to the maintenance stage.

Conceptual Design

Patients
Clinical
UX
Engineering
Data
Regulatory needs

Design Development

Patients
Clinical
UX
Engineering
Data

Production

UX
Engineering
Data
IRB

Launch Remediation

Patients
Clinical
UX
UAT
Engineering
Data

Maintenance

Patients
Clinical
UX
UAT
Engineering
Data

Good UX design takes into consideration the needs, abilities, and preferences of the target patient population; the clinical needs of the trial; and ultimately how the data will be analyzed. At Clincial ink, we work hard at this stage to fully understand the research goals. For example, if the application is to replace a paper diary, it’s important to not simply strive to replicate any paper form, but to understand the initial data capture goals behind the paper form.

How we Approach Good UX design at Clinical ink

The advanced technology and data sciences team at Clinical ink strongly believes in developing workflows, storyboards, and high-fidelity illustrations at the conceptual design stage to help partners visualize the tool and how it will be experienced by the patient. In tandem, we further refine this patient experience by conducting natural history studies to ensure that basic patient information, like quality-of-life factors, are designed for.

Ideally, the resulting design provides a single application that runs all the activities for the study, can be deployed in multiple modalities and across platforms (Android/iOS), and offers a seamless, engaging experience for the patient. Above all, this application should be intuitive; indeed, the gold standard for UX design is that it leads to a product that is usable, useful, desirable, findable (specifically, solutions to any product challenges can be found easily), accessible, and credible¹.

We’ve found that the best results integrate user-tested instruments, animation, calibration, and feedback directly into the study’s mobile application. Screen captures below from an electronic symptom diary illustrate how these elements can give patients all of the information and encouragement they need to complete assessments correctly. The instructions are clear, approachable, and thorough.  Our robust voice user research demonstrates that ensuring patients have the ability to practice, that they are alerted to any potential problem or interference, and that they can even listen to a preview of their recording ensures not only confidence in participation, but better data collection.

Summary of the Benefits of Good UX Design

Ensuring the best patient experience possible in clinical trials through careful attention to UX design pays dividends for drug sponsors.  When applications of sensors and wearable technology  are engaging and user friendly, they support greater trial participation and stronger protocol compliance, which has a direct impact on data quality as well as integrity. Adopting best practices in UX design also furthers the goals of health equity, ensuring that all can participate in research by reducing barriers and reducing the possibility that important information is excluded.

Learn more about how to harness the power of sensors and wearables in your clinical trial.

 ¹User Experience Design (semanticstudios.com)

Author

Joan Severson
Chief Innovation Officer,
Clinical ink

The post UX Design: A Prerequisite for Deploying Wearables and Sensors in Clinical Trials 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.

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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.