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Breaking Isolation: How Addressing Hearing Loss Can Preserve Social Connections Among the Elderly

A recent study has shown that providing hearing aids and advice on their use may preserve social connections among older Americans. The researchers found that those who received treatment for hearing loss retained one additional social connection on average over a three-year period compared to those who did not receive any hearing therapies.

The study, led by researchers at NYU Langone Health as part of the ACHIEVE clinical trial, revealed that those given hearing aids had more diverse relationships and maintained deeper bonds than those who were not treated for hearing loss. The findings add to evidence that helping aging patients hear better can enrich their social lives and boost their mental and physical well-being.

Experts have linked both loneliness and hearing loss to depression, heart disease, and early death, among other concerns. A 2023 report on the ACHIEVE trial showed that hearing interventions may slow cognitive decline among those at highest risk for dementia.

The research team collected data about older adults with untreated hearing loss across four sites in Maryland, North Carolina, Minnesota, and Mississippi. The study is among the largest to date to explore if hearing care can help prevent weakening of social networks, having included nearly 1,000 men and women ages 70 through 84.

Half of the participants received hearing aids, counseling sessions, and personalized instruction with an audiologist, while the other half received instruction about exercise, strategies for communicating with healthcare providers, and further resources for healthy aging. To measure social isolation, the researchers assessed how regularly participants spent time with others, the size and variety of their social networks, and the depth of their connections.

Among the findings, the study revealed that before treatment, participants in both groups reported feeling equally lonely. Three years after the intervention took place, loneliness scores slightly improved among those who had received hearing care, while scores slightly worsened among those who did not.

Hearing aids and related audiology appointments cost an average of $4,700, which is usually paid out of pocket, notes Coresh, the founding director of the Optimal Aging Institute. The authors plan to continue following the participants for another three years and to repeat the study with a more diverse group of people.

In conclusion, addressing hearing loss may be a crucial step in preserving social connections among older Americans. By providing hearing aids and related care, we can help alleviate loneliness, improve mental and physical well-being, and potentially slow cognitive decline. As the nation’s social isolation epidemic continues to grow, it is essential that we prioritize hearing health and incorporate hearing aid coverage into Medicare as a means of addressing this critical issue.

The University of East Anglia has conducted a study that reveals a crucial difference between essays written by students and those generated by AI tools like ChatGPT. While AI-generated essays are impressive in their coherence and grammatical soundness, they fall short when it comes to injecting a personal touch into their content. The researchers analyzed 145 essays written by real university students and another 145 generated by ChatGPT, comparing the two in terms of engagement markers – techniques that enhance clarity, connection, and persuade readers.

The study found that student-written essays consistently featured a rich array of engagement strategies, making them more interactive and persuasive. These included rhetorical questions, personal asides, and direct appeals to the reader. In contrast, ChatGPT-generated essays tended to be impersonal, mimicking academic writing conventions but lacking the conversational nuance and personal touch that human writers bring to their work.

The researchers attribute this difference to the nature of AI training data and statistical learning methods, which prioritize coherence over conversational nuance. This reflects a broader concern that relying too heavily on AI tools could lead to a decline in critical literacy and thinking skills among students.

Despite these findings, the study does not dismiss the potential role of AI in education. Instead, it suggests that tools like ChatGPT should be used as teaching aids rather than shortcuts. By harnessing the power of AI while preserving human engagement and creativity, educators can create a more balanced learning environment that benefits both students and teachers.

This research has significant implications for educators worldwide, particularly in spotting cheating and promoting critical literacy and ethical awareness in the digital age. The study’s findings highlight the importance of fostering personal touch and critical thinking skills in students, rather than relying solely on AI-generated content.

The faster planning system developed by MIT researchers uses machine learning to reduce solve time by up to 50 percent and produce a solution that better meets a user’s objective, such as on-time train departures. The new method could also be applied to other complex logistical problems like scheduling hospital staff or assigning airline crews.

Engineers often break down these kinds of problems into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. However, the overlaps cause many decisions to be needlessly recomputed, making it take much longer to reach an optimal solution.

The researchers’ new approach learns which parts of each subproblem should remain unchanged and freeze those variables to avoid redundant computations. A traditional algorithmic solver then tackles the remaining variables.

“This is a very complex combinatorial scheduling problem,” says Cathy Wu, a member of the Laboratory for Information and Decision Systems at MIT. “Our approach can be applied without modification to all these different variants.”

The researchers’ technique, which they call learning-guided rolling horizon optimization (L-RHO), teaches a machine-learning model to predict which operations should be recomputed when the planning horizon rolls forward.

To test their approach, the researchers compared L-RHO to several base algorithmic solvers and specialized solvers. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

Their method continued to outperform all baselines even when tested on more complex variants of the problem, such as factory machines breaking down or extra train congestion.

“Our approach can be applied without modification to all these different variants,” says Wu. “It even outperformed additional baselines we created to challenge our solver.”

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem – all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported by the National Science Foundation and MIT’s Research Support Committee, among others.