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mHealth devices and apps that continuously monitor patient symptoms and help with adherence also are likely to reshape medical practice – and with it the products pharma produce, writes Vicki Anastasi.
The rise of mHealth technologies, including mobile sensors, patient engagement apps and telemedicine, is reshaping how drugs are developed – and could greatly improve the efficiency of clinical trials. mHealth devices and apps that continuously monitor patient symptoms and help with adherence also are likely to reshape medical practice – and with it the products pharma produce. Co-development of drugs, devices and telemedicine are likely to become much more common.
The early successes of mHealth technologies in clinical trials suggest their use will continue to increase. For example, researchers report that a suite of smartphone apps used in clinical trials of a Parkinson’s disease treatment not only generated valid data from the field, but also were often more accurate than traditional office-based manual assessments, and sensitive enough to detect disease progression and drug effects.
In addition, the apps may have improved adherence with the study protocol. Patients were asked to complete six specific tasks every day that measured their balance, walking, dexterity, postural tremor, resting tremor, and steadiness of voice. Initially, 75 percent of requested data points were collected, and 50 percent were collected over the six months the trial ran, with 90 percent of patients running the tests at least once every four days – creating a much richer data stream than would be possible using traditional journals and office-based manual processes.
mHealth mobile sensors make possible continuous real-world monitoring of clinical signs such as blood sugar, blood pressure, oxygen saturation, heart rhythm or intraocular pressure. This leads to a better understanding of treatment effects, such as diurnal fluctuations, that are either too expensive or impossible to observe in a controlled study setting. Mobile devices also are better at detecting intermittent symptoms.
The possibilities of this richer data stream for improving trial efficiency are significant. Because mHealth apps are wirelessly connected, trial monitors may learn of adherence issues early and can move to address them. This may reduce subject attrition and the number of patients needed for a trial. In addition, the greater potential sensitivity of mHealth data collection may further reduce the number of subjects required to produce statistically valid results. Early efficacy, side effect and safety signals may also be detected, enabling protocol adjustments, earlier and better informed product development decisions as well as go/no-go decisions. This can reduce product development costs and cycle times.
mHealth apps and telemedicine may also support better patient engagement. Approaches such as virtual visits may be especially useful in studies where patients are less mobile, such as central nervous system conditions, heart failure, COPD and other conditions affecting older populations. Moreover, remote monitoring, along with virtual visits, is helpful for rare disease trials in which patients are often widely dispersed and may require a day or more of travel for an office visit. Such conveniences mean more patients can participate in trials, and are less likely to drop out, thereby reducing recruitment and overall study costs.
In the longer term, mHealth devices support the development and measurement of study endpoints that are more pertinent to patient needs. For example, mobility and daily activity of patients with COPD or heart failure may be measured directly by how many steps they take continuously in their daily lives rather than occasionally in the doctor’s office. In addition to helping focus product development directly on patient outcomes, this data can help make a value-based case for reimbursement by documenting improvements in patients’ lives.
However, new mHealth-derived outcome measures must be rigorously validated, and this requires extensive research and expertise. For example, is an improvement of 100 steps or 1,000 meaningful to the patient? Only detailed studies can answer such questions. Combining data from wearable devices with clinical measures and patient perceptions of how their progress affects their lives makes it possible to determine thresholds for change that can be used to construct robust, statistically validated, and patient-centered endpoints.
Developing reliable endpoints from mHealth devices also involves technical challenges. Investigators must ensure that devices, especially smartphones or wearables that patients provide, are reliable and supply valid data. An industry group known as the Critical Path Institute’s Electronic Patient-Reported Outcome Consortium is developing guidelines for ensuring the validity and reliability of new patient-centered outcome measures and endpoints derived from wearables data.
While many mHealth technologies for transforming clinical trials already exist, integrating them into a simple, seamless experience for patients is a challenge. Patients will not tolerate a large collection of apps for various functions, with an equally large collection of log-ins to keep track of. They need one easy-to-use interface that includes everything from motion detecting or other diagnostics, to self-report questionnaires, video visits, and tools to arrange clinic visits and transportation. A lot of work is being done to integrate these functions to make mHealth technology more user-friendly for both patients and sites.
On the product development side, the advent of mobile sensors for continuous monitoring of blood sugar, blood pressure, intraocular pressure and other clinical signs already make possible combined device-drug administration products such as smart insulin injectors and have many more potential applications.
Similarly, mobile sensors, interactive diagnostic apps measuring dexterity or cognitive function, and self-assessment apps for depression or other psychiatric conditions, may be linked with automated reminders and clinical monitors to improve therapy adherence or to supplement drug therapy with clinical and social supports that improve outcomes. Apps providing such support for smoking cessation and cancer treatment are already available, and are likely to become more tightly integrated with medications as they develop.
Consider eye drops, for example. Research shows that patient compliance with glaucoma and other chronic eye drop treatment falls sharply after the first few months. A motion sensor that can detect not only when a patient takes the drops, but also whether it is the patient or someone else applying the drops, and how many drops are used. The device can wirelessly communicate with a central computer that texts or calls patients with reminders, or even prompt a call from a nurse. This device-telemedicine combination has been shown to significantly improve patient medication adherence. It is currently in development for use in clinical trials, but has commercial potential as well.
Indeed, using such compliance enhancement systems in clinical trials may prompt regulators to require their use when the product hits the market as a condition of approval. This is because these systems may significantly affect clinical outcomes, which may lead regulators to view them as an integral part of the therapy. Planning for this in advance and developing sustainable compliance solutions may prove an effective market strategy that makes full use of new mHealth capabilities.
Data derived from mobile devices will also drive changes in medical care, from an office-based model to a continuous monitoring model. This may be especially valuable for understanding care pathways for patients with complex conditions. The care pathway not only includes the device or medication being used, but also all of the patient’s care activities, including physician visits, nutrition, tests and home care.
For example, a young patient with cystic fibrosis may have nearly 80 touch points a day involving multiple handoffs from family members to the school nurse to physicians, all to prevent infections and inflammation, and manage nutrition and lung function. The data from each interaction can be entered on a device and combined in the patient's electronic health record (EHR) to get a picture of his or her entire activity.
Continuous mHealth monitoring also may improve treatment of more common high-risk conditions such as heart failure and COPD. For example, sensors connected to analytic apps might detect exacerbations of heart failure based on weight gain and swollen feet, calling a nurse to intervene before hospitalization is required. Interactive apps could assist patients in managing diet and fluid intake. Predictive analytics might suggest medication or other changes based on disease progression models or the patient’s history before an acute exacerbation occurs.
Patients with more common chronic conditions such as Type 2 diabetes can benefit from mHealth technology as well. As patients progress from early stages that might be controlled with medication based on quarterly hba1c tests, to later stages requiring sophisticated drugs and multiple daily blood sugar tests, the value of continuous monitoring grows.
mHealth may be helpful for deciding among several treatment options quickly. For example, patients with gastrointestinal disorders such as ulcerative colitis are commonly placed on a trial of a new therapy for four to six months to see how they respond – but if it isn’t working, the patient is still experiencing discomfort. Mobile apps that report symptoms or check in with nurses could report problems earlier, making it possible to find a more effective therapy sooner.
These are just a few examples of how a consistent flow of data during a patient’s day-to-day life could provide a more holistic view of a patient’s condition, allowing earlier intervention and therapy customization, than is possible using sporadic data collected during clinic visits.
mHealth data also may help sponsors address emerging payment issues. As payers grasp the importance of understanding how patients make decisions – specifically, how their lives are affected by their disease – they are demanding evidence of a product’s value for cost.
Real-world data collected from patient devices can provide solid evidence of how a drug or combined drug-device-telehealth solution adds value beyond the existing standard of care. Combining mHealth data with EHRs, billing, insurance and pharmacy data makes it possible to assess not only how a new therapy performs directly, but also how it affects the overall need for all kinds of care, and total care costs. If a therapy can be shown to reduce overall care costs and improve patients’ function, an argument can be made for higher payment based on its demonstrated real-world value.
While it is necessary to ensure that continuous monitoring is ethical and does not impinge on patients’ rights to privacy and self-determination, when successfully implemented, mHealth solutions could realistically become the new standard of care. mHealth data will also generate insights for developing therapies that address needs that today may not even be known. For sponsors willing to embrace this holistic approach to developing drugs, devices and monitoring systems, the possibilities – and the benefits – may well be endless.
Vicki Anastasi is Vice President and Global Head, Medical Device and Diagnostics Research, ICON plc.