In a new study, researchers from Florida State University explore the challenges of recruiting and retaining older adults to participate in research.
The study also marks the first step in a broad, interdisciplinary effort by FSU to increasingly use artificial intelligence in research.
In the study published in The gerontologist, Associate Professor of Sociology Dawn Carr has identified key “motivational groups” among older people for research participation. Along with his 12 FSU-based co-authors, Carr suggests that identifying these groups — “pleasure seekers” and “seeking helpers,” for example — can guide recruitment and retention strategies.
“There is a lack of representation of older people in the research which leads to biased results,” Carr said. “Previous advice on how best to encourage older people to participate in research was uniform. Our research reveals that the motivations of older people are varied and complex.
Carr, the new director of FSU’s Claude Pepper Center, and study co-author Wally Boot, a professor in the Department of Psychology, said the lack of older adults in studies is prevalent throughout the research and has widespread consequences. They said personalized calls can increase the number and diversity of seniors participating in research.
“The characteristics of the people participating are important since we want to be able to generalize our findings,” Boot said. “And being able to recruit large samples of older adults is crucial; without large sample sizes, we cannot be confident in our results. »
This is the first study from a larger project funded by the National Institutes of Health (NIH). The Adherence Promotion with Person-centered Technology (APPT) project aims to understand participants’ daily motivations and schedules and provide just-in-time support to help them adopt behaviors that keep them in school.
The goal is to develop artificial intelligence-based reminder systems that encourage older people to participate in research related to aging.
“So much momentum and time is wasted when people drop out of studies, and clinical trials can fail because people don’t engage in the behaviors researchers ask them to adopt,” Carr said. “How can we test whether a behavioral intervention reduces the risk of cognitive impairment unless participants consistently engage in that behavior over the long term?”
Carr added, “To that end, we’ve already learned that there are older adults who have different motivations for participating: brain health advocates, research aides, fun seekers, and hobbyists. multiple motivations. We found that cognitive difficulties, age, employment status, and prior participation in research predicted membership in these categories.
Boot said AI approaches help predict the types of motivational messages that might resonate and keep attendees on track, but also the right time to deliver those messages.
“People have habits and we can learn the routine without being pushy,” he said. “When their adherence to the intervention begins to wane, we can detect this and provide a personalized motivational message at a time when we predict they are likely available to re-engage in the study.”
The study lays the groundwork for further use of artificial intelligence, Boot said.
“Two large clinical trials will provide a very rich data set to further develop algorithms to help predict who may be most at risk for poor adherence and the best suited approaches to re-engage them,” he said. “Ultimately, we may be able to predict and prevent failures and dropouts before they happen. This is only a first step towards very interesting possibilities.
For more information, visit https://doi.org/10.1093/geront/gnac035.