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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.

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:BackgroundPrevention programs, and specifically exercise, can reduce falls among community-dwelling older adults, but low adherence limits the benefits of effective interventions. Technology may overcome some barriers to improve uptake and engagement in prevention programs, although less is known on adherence for providing them via this delivery mode. We aimed to synthesize evidence for adherence to technology-based falls prevention programs in community-dwelling older adults 60 years and older. We conducted a systematic review following standard guidelines to identify randomized controlled trials for remote delivered (i.e., no or limited in-person sessions) technology-based falls prevention programs for community-dwelling older adults. We searched nine sources using Medical Subject Headings (MeSH) terms and keywords (2007-present). The initial search was conducted in June 2023 and updated in December 2023. We also conducted a forward and backward citation search of included studies. Two reviewers independently conducted screening and study assessment; one author extracted data and a second author confirmed findings. We conducted a random effects meta-analysis for adherence, operationalized as participants' completion of program components, and aimed to conduct meta-regressions to examine factors related to program adherence and the association between adherence and functional mobility. We included 11 studies with 569 intervention participants (average mean age 74.5 years). Studies used a variety of technology, such as apps, exergames, or virtual synchronous classes. Risk of bias was low for eight studies. Five interventions automatically collected data for monitoring and completion of exercise sessions, two studies collected participants' online attendance, and four studies used self-reported diaries or attendance sheets. Studies included some behavior change techniques or strategies alongside the technology. There was substantial variability in the way adherence data were reported. The mean (range) percent of participants who did not complete planned sessions (i.e., dropped out or lost to follow-up) was 14% (0-32%). The pooled estimate of the proportion of participants who were adherent to a technology-based falls prevention program was 0.82 (95% CI 0.68, 0.93) for studies that reported the mean number of completed exercise sessions. Many studies needed to provide access to the internet, training, and/or resources (e.g., tablets) to support participants to take part in the intervention. We were unable to conduct the meta-regression for adherence and functional mobility due to an insufficient number of studies. There were no serious adverse events for studies reporting this information (n = 8). The use of technology may confer some benefits for program delivery and data collection. But better reporting of adherence data is needed, as well as routine integration and measurement of training and skill development to use technology, and behavior change strategies within interventions. There may be an opportunity to rethink or reimagine how technology can be used to support people's adoption and integration of physical activity into daily life routines.

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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