Dataset Information

Modified STEADI Fall Risk Categories Predict Incident Cognitive Impairment.

ABSTRACT:

Background/objectives

It is unknown whether older adults at high risk of falls but without cognitive impairment have higher rates of subsequent cognitive impairment.

Design

This was an analysis of cross-sectional and longitudinal data from National Health and Aging Trends Study (NHATS).

Setting

NHATS, secondary analysis of data from 2011 to 2019.

Participants

Community dwelling adults aged 65 and older without cognitive impairment.

Measurements

Participants were classified at baseline in three categories of fall risk (low, moderate, severe) using a modified algorithm from the Center for Disease Control's STEADI (Stop Elderly Accidents, Deaths, and Injuries) and fall risk from data from the longitudinal NHATS. Impaired global cognition was defined as NHATS-derived impairment in either the Alzheimer's Disease-8 score, immediate/delayed recall, orientation, clock-drawing test, or date/person recall. The primary outcome was the first incident of cognitive impairment in an 8 year follow-up period. Cox-proportional hazard models ascertained time to onset of cognitive impairment (referent = low modified STEADI incidence).

Results

Of the 7,146 participants (57.8% female), the median age category was 75 to 80 years. Prevalence of baseline fall modified STEADI risk categories in participants was low (51.6%), medium (38.5%), and high (9.9%). In our fully adjusted model, the risk of developing cognitive impairment was hazard ratio (HR) 1.18 [95% CI = 1.08, 1.29] in the moderate risk category, and HR 1.74 [95% CI = 1.53, 1.98] in the high-risk category.

Conclusion

Older, cognitively intact adults at high fall risk at baseline had nearly twice the risk of cognitive decline at 8 year follow-up.

SUBMITTER: Crow RS

PROVIDER: S-EPMC8232824 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modified STEADI Fall Risk Categories Predict Incident Cognitive Impairment.

Crow Rebecca S RS Haudenschild Christian C Lohman Matthew C MC Roth Robert M RM Roderka Meredith M Masterson Travis T Brand John J Gooding Tyler T Mackenzie Todd A TA Batsis John A JA

Journal of the American Geriatrics Society 20210210 5

<h4>Background/objectives</h4>It is unknown whether older adults at high risk of falls but without cognitive impairment have higher rates of subsequent cognitive impairment.<h4>Design</h4>This was an analysis of cross-sectional and longitudinal data from National Health and Aging Trends Study (NHATS).<h4>Setting</h4>NHATS, secondary analysis of data from 2011 to 2019.<h4>Participants</h4>Community dwelling adults aged 65 and older without cognitive impairment.<h4>Measurements</h4>Participants we ...[more]

PMID: 33565618

Similar Datasets

Project description:BackgroundHigher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design.ResultsOverall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%).ConclusionFall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment.Trial registrationThis systematic review is registered in PROSPERO ( CRD42020171118 ).

Dataset Information

Modified STEADI Fall Risk Categories Predict Incident Cognitive Impairment.

Background/objectives

Design

Setting

Participants

Measurements

Results

Conclusion

Publications

Modified STEADI Fall Risk Categories Predict Incident Cognitive Impairment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets