2022 Pilot Project Updates

Dr. Jeungok Choi (Nursing) and Dr. Yeonsik Noh’s (Nursing and Engineering) project, Development and Usability Test of a Tablet-Based Lifestyle-Modification Intervention for Chinese Older Adults with Osteoarthritis, is a cognitive behavioral therapy-based tablet application (CBT-OA) targeting Chinese older adults with osteoarthritis. The team used a “culturally competent” approach; that is, they grounded the development of the application in an awareness of and respect for Chinese culture. The team stated, “Addressing five areas in lifestyle modifications recommended by the 2017 Chronic Osteoarthritis Management Initiative, the CBT learning modules [included] healthy diet, healthy weight, exercise including simple walking and mind-body connections, as well as ‘think about your thinking.” Specific tools within the modules were culturally relevant recipes, an activity log, a guide to exercise, and a video conferencing tool.

Consultants for this project were Christopher Martell, PhD, Director of the Psychological Services Center, Uhm Sueyeon, a mobile software developer, and Megan Cheung, RN, MSW, Associate Director/ Clinical Director in the Greater Boston Chinese Golden Age Center. Graduate research assistants were Kien To, Master’s student in computer science, mechanical engineering graduate Mihir Patki, and Miaomiao Shen, College of Nursing doctoral candidates.

The study is now complete, and it demonstrated that the CBT-OA tablet program is effective for managing arthritis symptoms.

Dr. Yeonsik Noh (Nursing and Engineering) and Dr. Cynthia Jacelon’s (Nursing) project, Home Healthcare Monitoring Based on Cloud Native Architecture, developed and evaluated a cloud native-based Healthcare Monitoring Platform (CN-HMP) that is ideal for remote home healthcare monitoring and relevant to the increasing global older population. As the population grows, so does the need for home healthcare services. CN-HMP is a distributed, elastic, and horizontal scalable system composed of wearable sensors that isolate states in a minimum of components. The wearable device collects health data in real-time from one or more patient users, and the data are then forwarded to healthcare providers through the CN-HMP. This is ideal due to its flexible scalability of users, medical parameters, and rapid and stable maintenance to enable an efficient response to crises.

Student research assistants were Shiyang Wang and Beizong (Max) Chen, graduates from the Electrical and Computer Engineering College, Abu Bony Amin and Ebenezer Asabre, doctoral students from the Electrical and Computer Engineering College, and Kourosh Alimohammadbeik, RN, College of Nursing doctoral student.

After evaluating the usability performance of the platform from the perspective of 14 nursing students, the completed study found that the platform was easy to use and effective. Stated the team, “By incorporating user-centered design principles, cloud-native architecture, and wear-able sensors, this pilot study has laid a foundation for developing [large-scale] home-based health-care platforms.”

Dr. Joohyun Chung (Nursing) and Dr. Xian Du’s (Engineering) project, an Unobtrusive Wearable EDA Sensor and Video-Recorded Body Movements to Assess Chronic Pain Among People with Alzheimer’s Disease and Related Dementias, addressed the need for innovative methods for chronic pain management in the Alzheimer’s Disease and Related Dementias (ADRD) population. Chronic pain is a prevalent symptom of people with ADRD that often goes undetected and untreated due to cognitive impairments and a reduced ability to communicate with caregivers.

The Chung-Du team developed a deep learning-based automatic pain detection algorithm to assess pain using EDA and video-recorded body movement to test the extent to which the physiological signs (such as heart rates, interbeat intervals, and EDA activity) and patterns of body movements correlate with pain scores by nurses’ direct observation among healthy adults, aided by engineering and robotics masters student, Meysam Safarzadeh. The team discovered that using the algorithm (in near real-time) at pain episodes allowed for adaptation of management and was effective in measuring the impact on outcomes of coping changes over the long term, demonstrating the potential for real-time interventions to provide the right advice at the right time to help chronic pain among people with ADRD.

Dr. Jeannine Blake (Nursing) and Dr. Juan Jiménez’s (Engineering) project, Research into IV Smart Pumps, Safety Standards, and Flow Rate Inaccuracy, addresses the need for more effective IV Smart Pumps. Adverse events associated with the use of IV smart pumps are among the most frequent sources of error reported to the US Food and Drug Administration. Flow rate inaccuracies in IV smart pumps occur when the actual medication flow rate does not match the rate programmed and displayed on the pump user’s interface.

This team has developed novel methods for measuring flow and pressure through IV Smart Pump systems during clinically relevant system setup conditions to collect better and understand flow rate accuracy. The resulting research determined that variables in IV smart pump setups (such as length of tubing or modifying the height of IV bags) can alter pressure within the IV smart pump system and affect flow rates that yield incorrect medication dosing. Their work includes the creation of a prototype design of a product designed to mitigate those variables.

Research is ongoing and has the interest of regulatory bodies involved in policy making and the interest of many companies who manufacture the IV smart pumps currently in use.