Dataset Information

Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology.

ABSTRACT: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately.To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults.The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies.Of the 119 participants (36% non-frail, 48% pre-frail, and 16% frail), 48 reported one or more fall (47% of non-frail, 33% of pre-frail, and 47% of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95% confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95% CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95% CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95% CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882).This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.

SUBMITTER: Mohler MJ

PROVIDER: S-EPMC5073011 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology.

Mohler M Jane MJ Wendel Christopher S CS Taylor-Piliae Ruth E RE Toosizadeh Nima N Najafi Bijan B

Gerontology 20160430 6

<h4>Background</h4>Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately.<h4>Objective</h4>To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults.<h4>Methods</h4>The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without bas ...[more]

PMID: 27160666

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Project description:ObjectivesTo examine whether physical frailty onset before, after, or in concert with cognitive impairment is differentially associated with fall incidence in community-dwelling older adults.DesignA longitudinal observational study.Setting and participantsData from 1337 older adults age ≥65 years and free of physical frailty or cognitive impairment at baseline were obtained from the National Health Aging Trends Study (2011‒2017), a nationally representative cohort study of US older adult Medicare beneficiaries.MethodsParticipants were assessed annually for frailty (physical frailty phenotype) and cognitive impairment (bottom quintile of clock drawing test or immediate and delayed recall; or proxy-report of diagnosis of dementia or AD8 score of ≥2). Incident falls were ascertained annually via self-report. Multinomial logistic regression was performed to estimate the association between order of first onset of cognitive impairment and/or frailty and incident single or repeated falls in the 1-year interval following their first onset.ResultsOf the 1,337, 832 developed cognitive impairment first (termed "CI first"), 286 developed frailty first (termed "frailty first") and 219 had co-occurrence of cognitive impairment and frailty within one year (termed "CI-frailty co-occurrence") over 5 years. Overall, 491 (34.5%) had at least 1 fall during the 1-year interval following the onset of physical frailty and/or cognitive impairment. After adjustment, "CI-frailty co-occurrence" was associated with a more than 2-fold increased risk of repeated falls than "CI first" (odds ratio 2.35, 95% confidence interval 1.51‒3.67; P < .001). No significant difference was found between participants with "frailty first" and "CI first" (P = .07). In addition, the order of onset was not associated with risk of a single fall.Conclusions and implicationsOlder adults experiencing "CI-frailty co-occurrence" had the greatest risk of repeated falls compared with those with "CI first" and "frailty first". Fall risk screening should consider the order and timing of onset of physical frailty and cognitive impairment.

Project description:So far, it has not yet been studied whether socioeconomic status is associated with distinct frailty components and for which frailty component this association is the strongest. We aimed to examine the association between socioeconomic status and frailty and frailty components. In addition we assessed the mediating effect of the number of morbidities on the association between socioeconomic status and other frailty components.This is a cross-sectional study of pooled data of The Older Persons and Informal Caregivers Survey Minimum DataSet in the Netherlands among community-dwelling persons aged 55 years and older (n = 26,014). Frailty was measured with a validated Frailty Index that consisted of 45 items. The Frailty Index contained six components: morbidities, limitations in activities of daily living (ADL), limitations in instrumental ADL (IADL), health-related quality of life, psychosocial health and self-rated health. Socioeconomic indicators used were education level and neighbourhood socioeconomic status.Persons with primary or secondary education had higher overall frailty and frailty component scores compared to persons with tertiary education (P < .001). Lower education levels were most consistently associated with higher overall frailty, more morbidities and worse self-rated health (P < .05 in all age groups). The strongest association was found between primary education and low psychosocial health for persons aged 55-69 years and more IADL limitations for persons aged 80+ years. Associations between neighborhood socioeconomic status and frailty (components) also showed inequalities, although less strong. The number of morbidities moderately to strongly mediated the association between socioeconomic indicators and other frailty components.There are socioeconomic inequalities in frailty and frailty components. Inequalities in frailty, number of morbidities and self-rated health are most consistent across age groups. The number of morbidities a person has play an important role in explaining socioeconomic inequalities in frailty and should be taken into account in the management of frailty.

Project description:Background Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. Methods We collected data from a wearable airbag’s inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort. Results The average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior–posterior (AP) falls (stroke-trained model’s F1-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P < 0.04). Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models. Conclusions These results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations. Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT05076565; Unique Identifier: NCT05076565. Retrospectively registered on 13 October 2021 Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-01040-4.

Dataset Information

Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology.

Publications

Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology.

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