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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
To evaluate the effectiveness of non-pharmacological multifactorial interventions on cognitive impairment in
Jiajia Cui1, Junjun Zhu1, Yanbin Wei2
1Department of Geriatrics, Lishui Second People's Hospital, No. 69, North Ring Road, Liandu District, Lishui, Zhejiang Province, China.
Abstract:
This prospective, cohort study was planned to assess the efficacy of nonpharmacological multifactorial approach for treating cognitive decline in patients of geriatric age group. This research involved 120 patients and caregivers, ≥ 65 years, with cognitive impairment, was a consecutive sample from a tertiary hospital. Measures involved learning and problem solving tasks, motor movement, company, and food choices that addressed participants' requirements. Cognitive status was evaluated by MMSE and MoCA, physical performance measured with TUG, depression with GDS and nutritional status with BMI and blood sample. Subsequent evaluations were done at 3 month interval for 1 year. At the end of 12 months, improvement in cognitive function was noted by a mean of 4.0 points in MMSE, and mean of 3.8 points in MoCA. Physical fitness, depression status, nutritional status, social integration and sleep quality were also found to have significant changes across time. Also, the studies revealed an enhancement of patients' satisfaction as well as a reduction in caregiver burden. These results raise the possibility that approaches targeting multiple risk factors of cognitive decline can effectively promote cognitive function and general quality of life in elderly people.
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