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Voice recorders emerge as new tool in falls monitoring

Wrist-worn voice recorders have been found to be a useful intervention in better understanding what happens when people lose their balance.
A study has concluded that, among older adults, voice recorders are effective at capturing the circumstances and context in which they lost their balance and potentially fell, without relying on recall later.
The study, led by Michael Madigan in the College of Engineering, builds on years of his own foundational work and prior research conducted by the University of Michigan Medical School.
“In the past, researchers would ask participants to recall what they were doing when they lost their balance, but memory can be unreliable,” said Michael Madigan of Virginia Tech College of Engineering.
“With this new method, participants record their experiences immediately after an incident, providing much more accurate and detailed information.”
In the study, 30 participants wore voice recorders on their wrists over the course of three weeks, and in the event of balance loss, turned them on to record answers to a handful for questions.
They included when and where the balance loss occurred, what they were doing at the time and how they attempted to regain their balance—did they grab a railing, take steps, or sit down?
This immediate, self-reported data was analyzed by Madigan and his team. Instead of waiting to meet with researchers after losing their balance, participants could reflect on what happened in the moment.
“We’re trying to better understand the circumstances in which people lose their balance,” Madigan says. “This process doesn’t require people to think back weeks or months to an incident, especially when memory can be unreliable.”
Maria Moll, a retired epidemiologist and study participant, found the research particularly meaningful, especially as someone in her 70s who remains physically active. After a friend experienced a fall, Moll became more interested in contributing to balance-loss prevention research.
“I’ve always been interested in physical fitness and balance, especially as I age,” says Moll. “This study made me more mindful of my movements, particularly during more challenging activities like hiking.”
Looking ahead, the team plans to expand the study to larger groups and combine the data with other lab-based measurements.
By doing so, they hope to identify individuals who are most at risk of balance loss and develop strategies to proactively address those risks.
“We want to give clinicians the tools to intervene before a fall occurs,” says Madigan. “This method can provide more reliable, detailed information that helps us understand not just how people lose their balance, but why.”
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Research
AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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