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Risk factors for nursing home admission among older adults: Analysis of basic movements and activities of daily living.

Akira SagariTakayuki TabiraMichio MarutaKoji TanakaNaoki IsoTakuhiro OkabeGwanghee HanMasahiro Kawagoe
Published in: PloS one (2023)
This retrospective study aimed to clarify the risk of older adults' nursing home placement in terms of basic movements and activities of daily living (ADLs) by analyzing data from a long-term care insurance certification survey in 2016‒2018 in City A. Of the 21,520 people certified as needing care, 16,865 could be followed up until 2018. Data on sex, age, household structure, and level of care required were obtained. Those who lived at home and at nursing homes were categorized as the "Unchanged group" and the "Changed group," respectively. Multivariate binomial logistic regression analysis was performed, with group type as the dependent variable and basic movement and ADL scores as the independent variables. For factor analysis according to care level, participants were classified into support need levels 1 and 2, care need levels 1 and 2, and care need levels 3, 4, and 5. For those categorized into support need levels 1 and 2, standing on one leg and transferring (basic movements) and urination and face cleaning (ADLs) were associated with nursing home placement. For those in care need levels 1 and 2, getting up and transferring (basic movements) and bathing, urination, face cleaning, and hair styling (ADL) were significantly associated with nursing home placement. For those in care need levels 3, 4, and 5, sitting and transferring (basic movements) and self-feeding and defecation (ADL) were significant. Occupational therapists must focus on older adults' declining ADLs and basic movements and relay the necessary information to patients, families, and other healthcare professionals to ensure appropriate and prompt care delivery.
Keyphrases
  • healthcare
  • palliative care
  • quality improvement
  • long term care
  • physical activity
  • newly diagnosed
  • machine learning
  • data analysis
  • deep learning
  • chronic pain
  • big data