Advances in healthcare and medical treatments have led to longer life expectancies in many parts of the world. As people receive better healthcare and management of other health conditions, they are more likely to reach an age where neurodegenerative diseases become a greater risk. Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), are complex and can affect various aspects of a person's cognitive, motor, and sensory functions.
Multimodal assessment allows healthcare professionals to assess multiple domains of functioning, providing a comprehensive evaluation of the disease's impact on an individual. Wearables can play a pivotal role in multimodal assessment by objective, sensitive, and continuous assessments of both upper and lower extremity functioning (see the sidebar, “Wearable Sensors”). This is important because large amounts of objective data acquired at a high frequency in a real-world setting could facilitate personalized and precision care with improved health outcomes.
Wearables can engage patients in their care by providing them with meaningful data and feedback on their health and wellness. This can motivate individuals to take an active role in managing their health, leading to better outcomes. For instance, in a research study conducted at VA Houston, Freytag et al. demonstrated that healthcare professionals can utilize remote physical activity monitoring based on wearables to confirm the adherence of individuals with cognitive impairment to self-defined goals. These goals may include activities like walking the dog in the evening or increasing engagement with grandchildren.1
Similarly, there are many studies demonstrating the potential of using wearables for personalized care by real-time sensing of heart rate, blood pressure, blood glucose, and sleep patterns. Continuous glucose monitoring, for example, can provide insights into post-consumption blood glucose spikes following the ingestion of sugar-rich foods. Prolonged elevated glucose levels may signal an elevated risk of developing insulin resistance and chronic diabetes in the future.
Moreover, recent advancements in artificial intelligence (AI) have unlocked the ability to process extensive datasets for the early detection of diseases or health deteriorations. For instance, AI can discern irregular activity patterns, which may serve as indicators of conditions like abnormal circadian rhythm, depression, frailty, or severe fatigue.2 Examples of these irregular activity patterns may include extended periods of daytime bed rest, disturbances during nighttime sleep, or reduced number of sit-to-stand transitions. The reduced number of sit-to-standing transitions may be an indicator of frailty among older adults.3 Timely detection paves the way for prompt interventions and improved treatment outcomes. Overall, the use of wearables can facilitate personalized care and promote patient empowerment by providing individuals with the tools and information they need to actively participate in managing their health and well-being.
Furthermore, wearables can be valuable tools for care coordination and reducing caregiving burden. Wearables consisting of an accelerometer along with a sophisticated algorithm have been employed for fall detection. In the event of a fall, the device can automatically alert caregivers or emergency services, ensuring immediate assistance in case of accidents or injuries.
Caregivers can share the data collected by wearables with healthcare providers to aid in care coordination. This information can be especially valuable during medical appointments or when adjustments to the care plan are necessary. Data collected by wearables can assist healthcare providers and caregivers in making informed decisions about care plans. For example, if a wearable indicates a decline in activity levels, adjustments can be made to the care plan to encourage more physical activity. In a study conducted at the Baylor College of Medicine, Mishra et al. explored the challenges and acceptance of digital health technology as a means to alleviate the caregiving burden associated with dementia.4 One caregiver shared her experience of being frequently away from home due to her job, while her mother, who suffers from dementia, often forgets to engage in the sole physical activity available to her, which involves going to the gym. By utilizing wearables, this caregiver would experience peace of mind and gain the capability to monitor her mother's physical activity levels effectively. The sidebar, “A Digital Health Platform” describes an example of how multiple care partners can collaborate on managing and coordinating the care of their loved ones.
Lastly, improved health outcomes are of particular interest for pharma companies to reduce the time and cost of early-stage clinical trials. Recent advancements in wearable technology, including improved battery life, increased on-board memory, and enhanced sensor durability (such as waterproof packaging), have made wearables an appealing choice for remote patient monitoring.
PAMSys Voice. The PAMSys Voice™ wearable sensor incorporates bidirectional microphones for synchronized audio data capture, as well as an accelerometer and gyroscope to record chest motion data. The innovative patented device seamlessly integrates audio and motion monitoring, making it a unique wearable sensor for remote tracking of speech activities and biomarkers, coupled with precision actigraphy and fall detection capabilities. BioSensics received a $3 million, three-year award from the U.S. National Institutes of Health (NIH) grant to support the development of the device.
The PAMSys sensor includes advanced algorithms to detect non-compliance of participants in wearing the sensor. The sensor has a very long battery life (up to 6 months) and large memory capacity to record both raw and analyzed data and can be worn as a pendant or on the wrist. The device uses sensor-integrated ePRO technology and BioDigit Cloud. The architecture of the system is fully flexible and allows modular deployment of system components (from using PAMSys sensors only for passive monitoring to full system deployment for real time data access).
LEGSys™ and BalanSens™ are first FDA-registered wearable devices for objective assessment of gait, balance, fall risk, and mobility.
LEGSys. LEGSys is a portable medical device based on wearable sensors for quick and objective assessment of gait, gait steadiness, and dynamic balance during walking. LEGSys includes software modules for administrating standard gait tests such as Timed Up & Go (TUG) and 6 Minute Walk Test (6MWT). During each test, LEGSys sensors accurately measure the kinematics of lower body to calculate a wide range of gait parameters.
Advanced algorithms automatically check, detect, and correct possible human errors (e.g., sensor misplacement). In addition, customized versions of LEGSys enable assessment of endpoints such as foot drop and wrist kinematics during walking, including spatio-temporal gait parameters and gait initiation.
Spatio-temporal gait parameters:
Step and stride length
Step and stride time
Step and stride time and length variabilities (indicators of gait unsteadiness)
Shank, thigh, and knee range of motion (indicators of joint rigidity)
Center of mass sway in medial-lateral and anterior-posterior directions (indicators of dynamic balance during walking)
Number of steps to achieve steady state walking
Distance required to achieve steady state walking
Spatio-temporal parameters of gait during gait initiation
BalanSens. BalanSens is a portable medical device based on wearable sensors for quick and objective assessment of balance and postural sway. BalanSens includes software module for standard balance tests. BalanSens uses two motion sensors (one on the shin and one on the waist) to calculate more than 17 measurements, including:
Center of Mass (COM) motion
Ankle and hip angles
Reciprocal Compensatory Index (RCI)
These wearable devices enable remote monitoring of instrumented activities of daily living and life space and enable continuous monitoring of cognitive decline in older adults with dementia.
Wearables enable remote monitoring of trial participants, allowing researchers to collect data without the need for participants to visit clinical sites regularly. This is particularly valuable for large-scale, global trials and when dealing with patients who have limited mobility. This reduces the number of clinical visits, minimizing the associated cost.
The consistency and accuracy of data collected through wearables can enhance the quality of trial data, making it easier to identify trends, assess treatment efficacy, and draw meaningful conclusions. Participants in clinical trials may drop out for various reasons, including inconvenience or adverse effects. Wearables can reduce dropout rates by making data collection less intrusive and more convenient.
Challenges to Overcome
There are, however, three critical challenges that need to be addressed when developing wearable devices: ensuring consistent use, affordability, and patient privacy.
First, ensuring that patients or users consistently wear and use the devices as intended can be challenging. Many users stop using wearables after a short period due to discomfort, inconvenience, or a lack of perceived benefit. Therefore, focus should be given on improving the ease of using the technology among the patient population of interest.
Second, the cost of wearables, especially medical-grade devices, can be a barrier to adoption, both for individuals and healthcare organizations. Insurance coverage and reimbursement policies for wearables need to be addressed.
Third, wearables collect sensitive health data, raising concerns about privacy and security. Protecting this data from unauthorized access, breaches, or misuse is a significant challenge. Compliance with data protection regulations like HIPAA (in the United States) and GDPR (in Europe) is essential.
A Digital Health Platform
One digital health solution, called Nili, is designed to support the needs of seniors and patients living independently, while minimizing the burden of caregiving. Through an engaging tablet companion, Nili enables aging in place and activities of daily living management for older adults and patients, including individuals with dementia. Using the Nili Mobile App, multiple care partners can collaborate on managing and coordinating the care of their loved ones.
Nili was developed in collaboration with experts at Baylor College of Medicine and funded by a $3 million award from the National Institutes on Aging (NIA). The platform is based on the latest research in dementia care, including a recent study published in Gerontology. The study found that coordinated care and support for individuals with dementia and their care partners can improve outcomes and reduce burden. A larger randomized clinical trial is underway (ClinicalTrials.gov Identifier: NCT04308512).
The role of multimodal assessment, particularly using wearables, in providing comprehensive evaluations of individuals’ cognitive, motor, and sensory functions is critical as advances in healthcare lead to longer life expectancies. There is great potential to use wearables for remote physical activity monitoring, personalized care through real-time sensing of various health parameters, and the integration of artificial intelligence for early disease detection. Wearables can improve health outcomes, facilitate personalized care, and reduce the time and cost of early-stage clinical trials for pharmaceutical companies.
To maximize the potential benefits of wearables in healthcare, it will be imperative for wearables developers to find ways to ensure consistent use of wearables by patients, ensure the affordability of medical-grade devices, and address concerns surrounding patient privacy and data security.
- Freytag, Jennifer, et al. “Using wearable sensors to measure goal achievement in older veterans with dementia.” Sensors 22. 24 (2022): 9923.
- Li, Liqing, et al. “Daytime naps and depression risk: A meta-analysis of observational studies.” Frontiers in Psychology 13 (2022): 1051128.
- Parvaneh, Saman, et al. “Postural transitions during activities of daily living could identify frailty status: application of wearable technology to identify frailty during unsupervised condition.” Gerontology 63. 5 (2017): 479-487.
- Mishra, Ram Kinker, et al. “Care4AD: a technology-driven platform for care coordination and management: acceptability study in dementia.” Gerontology 69. 2 (2023): 227-238.
This article was written by Ram kinker Mishra, PhD, Senior Research Scientist at BioSensics, Newton, MA. For more information, e-mail