New program reduces burnout in dementia caregivers, novel research evaluation proves

As the old saying goes, you cannot pour from an empty cup. To give time and energy to others, it is vital to attend to one's own well-being. As the U.S. population ages, there is a growing demand for tools that support the more than 19 million people who serve as caregiversof family members with Alzheimer's Disease and related dementias (ADRD). Family caregivers of individuals with ADRD are vulnerable to poor mental and physical health due to long-term, strenuous caregiving and lack of support.

A novel process (i.e., delivery and functionality) evaluation developed by Kang Shen, PhD health services researchstudent at George Mason University's College of Public Health, establishes that user-friendly digital interventions are the solution. WECARE 2.0, a culturally tailoreddigital health program, is proof as it has enhanced caregivers' physical and mental health.

"WECARE's approach-combining cultural relevance with an accessible, widely used technology app-proved to be highly effective and well-accepted by users. High user satisfaction, participant retention, and significant improvements in caregiving skills and positive aspects of caregiving all point to WECARE's promising early results," said Shen, who is a first-generation student and also has a bachelor's and master's degrees in health informatics from George Mason.

The study, Process evaluation of a digital health intervention for dementia caregivers: Integrating active and passive measurements,was published in September 2025 in Alzheimer's & Dementia,the premier journal of the Alzheimer's Association and the leading journal in the field.

Participants became more knowledgeable about ADRD, learned problem-solving strategies, improved their personal caregiving skills, reduced stress, increased social support, while being user-friendly.

"This program [WECARE] is so good, easy to understand, even for a 70-year-old. I don't like reading text and can play the recordinginstead. I like your how-to videos; I can open them whenever I want and watch them multiple times," one program participant shared.

WECARE, which was originally designed by Y. Alicia Hong,a Department of Health Administration professor and digital technologies intervention researcher and specialist, was delivered through the social media app WeChat. Through the platform, researchers provide easily accessible educational articles and videos, tips on practicing self-care, and opportunities for social connection with other caregivers.

Innovative Evaluation Methods

Shen's process evaluation of WECARE 2.0 is the first to employ multiple-qualitative and quantitative-data collection methods.With data from surveys, interviews, self-reported reviews, and website engagement, Shen measured how often participants opened the app and its accompanying resources, how long they spent using the resources, and feedback of the program at the intervention's conclusion.

Shen's approach also highlights the importance of creating tools that do not simply exist but are observable to implementers for timely improvement. Unlike previous evaluative methods, her use of short surveys (active measurement) and tracking user activities on the backend (passive measurement) offers program developers comprehensive insight into what works, what doesn't work, and why throughout the intervention process.

This innovative process evaluation method can facilitate developing more effective digital health interventions for underserved dementia caregivers. Results are being integrated into the next version of WECARE and can inspire future interventions.

"This study serves as a framework for creating and evaluating digital health tools that are culturally grounded and genuinely accessible for diverse, underserved caregiving communities. Our goal is to inspire a shift toward more inclusive and effective support systems that meet caregivers where they are, leveraging familiar technologies to overcome access barriers," said Shen.

Her research was supported by Hong. Additional co-authors include Yixuan (Janice) Zhang from the College of William and Mary Department of Computer Science, Hae-Ra Han from the Johns Hopkins University School of Nursing, Jessica Linfrom the George Mason's Department of Computer Science, and Kenneth Hepburn from the Emory University School of Nursing.