Malnutrition continues to be a widespread issue in Australian aged care homes, but Griffith University researchers are striving to address the problem through the use of an early diagnostic tool.
Dr Marie-Claire O’Shea from the School of Health Sciences and Social Work worked with colleagues at Monash University to develop the Automated Malnutrition (AutoMal) screening tool.
“Malnutrition is a state resulting from inadequate intake or uptake of nutrition that leads to loss of fat stores or muscle mass, leading to diminished physical and mental function,” Dr O’Shea said.
“It’s a serious problem affecting Australian aged care facilities with an estimated 40 – 60% of residents diagnosed as being malnourished, so the time is now address this national problem.
“AutoMal has been designed specifically for aged care homes with planned testing to expand to in-home care settings.”
AutoMal diagnoses malnutrition by measuring BMI and weight change over the course of six months.
It calculates the predicted probability of malnutrition using a formula from which a threshold value is applied as either malnourished or not-malnourished.
Dr O’Shea said while there are screening tools currently available, they’re reliant on training and can take substantial time particularly when requiring data collection from residents or care staff.
“Malnutrition screening tools are only useful if they are used,” she said.
“We’ve designed AutoMal to be as intuitive as possible, requiring substantially fewer resources than existing screening methods.
“Automated malnutrition screening would enable data to be reported regularly thereby increasing accountability, and promoting quick nutritional intervention.
“AutoMal has the potential for widespread implementation and may substantially enhance efforts for identifying malnutrition, a critical step in malnutrition treatment and maintaining well-being of long-term care residents.”
The paper ‘An automated malnutrition screening tool using routinely collected data for older adults in long-term care: development and internal validation of AutoMal’ has been published in The Journal of the American Medical Directors Association.