Resistance training and data review shown to help reduce falls, new studies show

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Published On: August 11th, 2023Categories: Education & ResourcesTags: , , ,

Two new studies point to resistance training and patient data analysis to help reduce falls. 

According to a study published in the Journal of Sports Sciences, participating in eight weeks of a scripted exercise program improved clinically relevant indicators of falls risk in previously sedentary older adults. It also helped attendees to start new exercise habits that they sustained one year later.

The participants, 60 or older, with sedentary lifestyles, were randomly assigned to one of three groups: a sedentary control group, a walking group that met twice weekly, and a group that participated in Stay Strong, Stay Healthy (SSSH), a progressive resistance training program, twice weekly for eight weeks.

SSSH participants demonstrated greater absolute changes in sleep quality, activity, 30-second-sit-to-stand and upper-body flexibility than did those in the walking or control groups. Twelve months later, SSSH participants reported significantly increasing their independent resistance (+68 minutes), aerobic (+125), and flexibility (+26) training minutes per week.

In addition, after the eight-week intervention, 72% of SSSH participants migrated from an at-risk fall category to normal, compared with 27% and 33%, respectively, in the walk and control groups. 

Another study published by an academic team shows that already existing data can accurately predict patients’ fall risk and help nursing home caregivers better target prevention strategies. 

Staff turnover is at an “extreme high,” which can deter frontline workers from quickly identifying residents most likely to suffer falls. The INJURE-NH tool developed by the team can be used to embolden clinical care through automated model calculations based on data in the Minimum Data Set. 

However, the study noted that many facilities have “limited” IT infrastructure, making it “preferable” for the Centers for Medicare and Medicaid Services to modify resident assessments to allow automated calculations. 

The new model uses nine essential predictors to assess six-month and two-year fall risk more accurately for all nursing home residents. 

The “core predictors” are: gender, age, visual impairment, cognitive impairment as measured by the Cognitive Function Scale, ADLs, orthostatic hypotension, diabetes, history of hip fracture, and recent falls.

The study cohort was composed of nursing home residents in the U.S. from Jan 1, 2016, to Dec 21, 2017, who had a residency of more than 100 days with fewer than 10 consecutive days outside of the facility.