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When it comes to understanding how wearables are changing healthcare, consumer brands serve as a solid leading indicator. Popularized by brands like Apple Watch, FitBit, and Garmin, the global wearable healthcare market was estimated at $16.2 billion in 2021, and is projected to double in the next five years. Healthcare wearables in daily life Although most users rely on healthcare wearables to check texts during spin class or crush their friends’ daily step records, an increasing number of users rely on smartwatches and other medical wearables for life-saving medical information. As the technology evolves, healthcare wearables can now give minute-by-minute EKG readings, monitor blood sugar, check oxygenation levels, and help people use real-time data to manage their health while they go about their regular activities. Wearables also deliver oversight and peace of mind to caregivers, as when diabetic children wear devices that monitor insulin and food intake and link to mobile apps monitored by their parents. These breakthroughs allow patients of all ages more autonomy while providing reassurance to caregivers that the person is safe. Learn more about what wearable devices make possible >> Healthcare wearables in long-term care settings Long-term care presents a gap between that at-home monitoring scenario and the tech-saturated acute care space of a hospital or clinic. Historically understaffed, nursing homes and long-term care facilities struggle with high turnover, increasing rates of preventable errors, and unnecessary escalation of avoidable medical events. In addition to the impact on the patient and their loved ones, these realities impact the facility itself through lower reimbursement rates and increased cost of care.   A 2021 American Healthcare Association and the National Center for Assisted Living survey on staffing in these facilities showed that 99% of nursing homes and 96% of assisted-living facilities face a staffing shortage. Harvard University professor David Grabowski says the pandemic only worsened that already critical situation. He notes, “We’ve overlooked and undervalued this workforce for a long time and now we’re at a full-blown crisis…We’re in a crisis on top of a crisis.” Ensuring the right level of care for high-risk and elderly patients amidst staffing constraints formed a critical use case for transformation. Healthcare wearables emerged as a leading option that would give staff the ability to monitor more patients, get notifications when care is needed, and escalate when necessary. Overcoming obstacles to adoption Implementing a program for wearable devices in nursing homes introduced more stringent requirements than consumer wearables, including: Privacy protection: Patient medical information, covered under HIPAA, requires more protection than off-the-shelf iOS and Android systems offer. Usability concerns: Patients under care in nursing homes and long-term care facilities often lack experience with technology, and/or the dexterity to manage new devices. Cost considerations: In addition to the cost of patient wearables and devices allowing nursing staff to monitor and communicate alerts, facilities must also invest in secure data infrastructure and information architecture beyond standard integrations in market-ready smartwatches. Creating a targeted solution Realizing that nursing home and long-term care facilities faced unique barriers to implementing wearable devices, BioLink Systems set out to create a solution. Initially, the company devised a device that could be attached to an adult brief to monitor urination levels and body position. However, early issues with the prototype limited production scalability. Fusion worked with BioLink to architect a cloud-based IoT solution that uses machine learning to exceed the company’s initial vision. Designed with a minimalist aesthetic and user experience to fit the target demographic, the BioLink bracelet and adult brief wearables: Meet HIPAA requirements Monitor patient fluids Track patient vital signs Alert nursing staff when patient vitals fall outside their customized range Escalate alerts if patients are not attended within an allotted timeframe Initial testing and rollouts in nursing homes delivered immediate results, including: Improved patient care Decreased response times Fewer avoidable events such as medication errors  Decreased escalation of care level, including hospitalizations  Improved oversight Increased compliance with state, federal, and agency regulations  Better experiences for the patient and their loved ones Learn more about how BioLink’s wearables are changing healthcare >> What’s next for wearable healthcare devices   As facilities gather more data from using these devices, the machine learning algorithm BioLink and Fusion designed will continue to refine unique vital sign ranges for each patient, resulting in more targeted care. Future iterations of the BioLink device will integrate that information with the patient’s electronic medical record, enabling further customization of care. While each device starts with a baseline for normal with each of these vital signs, the more data that is collected, the better the facility can care for the patient. For example, if a patient’s oxygen level is continuously high, the device eventually creates a new threshold for that patient’s vitals and only sends notifications accordingly. Especially with the elderly population, there are many people that can’t communicate what they need or when they feel a certain way. There are endless possibilities for being able to provide better care under these circumstances. With options like dehydration sensors, nursing care staff is better able to not only bring water to patients but ensure that they are actually consuming it. The more variables, the better Ultimately, the more variables, the better the information — resulting in better care and better outcomes. The correlation and combination of all the data from a patient can detect changes and allow for more timely, preventative care. And the more information included, the better the insights from the algorithm. With the right information, staff can prevent different medical events by predicting problems and eventually creating better remedies and treatments to avoid costly medical interventions or catastrophic incidents. As industry leaders and healthcare facilities see the impact of devices like BioLink’s bracelets, we expect to see greater adoption of healthcare wearables to elevate patient care, reduce facility costs, and find operational efficiencies even during times of staffing crises. With the right technology and innovation, we can change outcomes and save lives — with a wristband.

Advances in mobile health technology have transformed the entire landscape of healthcare, including the ability of physician groups, employers, nursing home facilities, and pharmaceutical companies to capture data in the healthcare space. Gone are the days of paper tracking for your glucose levels and blood pressure. Instead, wearable devices like watches and trackers can seamlessly provide real-time data streams to applications and third parties. The data collected from wearables can be used for clinical research, patient monitoring, and wellness tracking, among other uses. Each data point collected can add complexity to your broader data set. Because of the amount and complexity of data, turning to machine learning (ML) can help organizations leverage their data to identify patterns and make data-driven decisions. By applying machine learning techniques to wearable device data, we can now surface patterns in big data and make predictions about behavior. Machine learning enables healthcare-related industries to leverage wearable device data and identify trends, improve recommendations, and define research outcomes. Popularity of wearable devices Wearables are popular, and their adoption continues to grow. Globally, the wearable technology market is expected to grow from $69 billion in 2020 to $81.5 billion in 2021, an 18.1% increase, according to the latest forecast from Gartner. What’s fueling the growth? Demand for smart devices in the healthcare sector is rising, as is demand for Internet of Things (IoT) devices. Many devices are not fitness-specific, featuring notification of text messages, push notifications for mobile apps, and the ability to pay for items by scanning a QR code with Google Pay or Apple Pay. As such, they have broad appeal. “As a result of the pandemic, we have seen wearable devices become much more than just activity trackers for sports enthusiasts. These devices are now capable of providing accurate measurements of your health vitals in real time. Improved measurement accuracy coupled with the latest advancements in ML make it possible to detect abnormalities before they lead to a major health event.” – Alex Matsukevich, Fusion Alliance Director of Mobile Solutions Types of data collected by wearable devices There are a variety of brands and categories of wearable devices, from mass-market consumer versions to highly specialized types created for niche uses. Apple, Fitbit, Google, Samsung, Garmin, LG, Sony, and Microsoft dominate the market. Though the concept of “wearables” includes a focus on wristwatches, while exercise equipment, glasses, and textile sensors are also becoming more common. Wearable devices can measure: Sleeping patterns Heart rate Irregular heart rhythms Location/route during exercise Pace, stride, and distance while moving Blood oxygen levels Falls Limitations of wearable device accuracy Wearables do have limitations, and accuracy is a concern. Healthcare decisions made using erroneous data could have outcomes detrimental to a patient’s overall health. A study from the University of Michigan reviewed 158 publications examining nine different commercial device brands. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate, and Fitbit tending toward underestimation. But for energy expenditure (calories burned), no brand was deemed accurate. This does not mean that the results are invalid, but that there is a significant difference between results from wearables and clinical results in a lab setting. Wearable devices are constantly upgraded and redesigned as technology improves. And data collected by wearables does not provide a clinical diagnosis. As such, this data is just part of the larger picture of health and can be used only in conjunction with other factors to evaluate your overall wellbeing. Overcoming the biggest challenge of wearable device data analysis Healthcare professionals are already using ML to analyze data for patients. Research published in the International Journal of Research and Analytical Reviews confirms that ML techniques are successful in predicting health conditions such as heart disease, diabetes, breast cancer, and thyroid cancer. The biggest hurdle to incorporating device data into broader data sets is the addition of new inputs, such as hours of sleep or total steps walked per day. Traditional data points such as total cholesterol or blood pressure readings are less frequent, so there is a smaller amount of data overall. The challenge is finding how to best incorporate it into other data sets to create a more comprehensive picture of health. The future of wearable device data and machine learning We can glimpse into the future of wearable device data and machine learning with Microsoft’s recent patent filing. Their potential product aims to provide wellness recommendations based on biometric data, such as blood pressure and heart rate, pertaining to work events. To do this, Microsoft requests access to applications used by employees. Microsoft then tracks data points such as: Duration of time spent writing emails Number of times a user refreshes their inbox Time spent reading emails Number of corrections made when writing emails Recipient list for emails Number of meetings in a day Tone of language in emails By combining this information with biometric data (from a secondary device such as a Fitbit or Apple Watch) and machine learning, Microsoft could begin to understand what work events trigger a response. For example, suppose an employee received an email from their manager.  Microsoft might observe that the employee spent a higher-than-average amount of time reading the email and that the employee’s heart rate was also elevated during this time.   Based on these insights, Microsoft could propose recommendations for helping employees manage stress levels, highlighting events that trigger anxiety. Patent filing sample image from Microsoft outlining tips and recommendations to improve employee wellness With a broad user base using both Office and Teams already, Microsoft has a deep understanding of work-related events. As Facebook built their business making sense of our social lives, Microsoft has the potential to optimize our work lives. “Wearables combined machine learning will become the new standard in personalized consumer electronics, rapidly increasing in popularity and scale every year until then. An integrated device of the future will be able to get a baseline of your health and will alert you to any abnormalities present. We already see this happening with the new Apple Watch, and it will be very soon that this technology becomes commonplace.” – Michael Vieck, Fusion Alliance Software Developer Wearable devices will transform healthcare experiences Data is the key to predicting, understanding, and improving health outcomes. IBM Research anticipates that the average person will generate more than 1 million gigabytes of health-related data in their lifetime, equivalent to 300 million books. The sheer volume of data means that machine learning will be vital in making sense of it. Paired together, wearable devices and machine learning have the potential to transform healthcare experiences. Today’s applications and uses are only the beginning. Read more: Top 3 reasons to invest in machine learning for mobile Machine learning and wearable devices of the future Wearing Your Intelligence: How to Apply Artificial Intelligence in Wearables and IoT