Dataset Information

The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study.

ABSTRACT:

Background

It remains uncertain whether the association between physical activity (PA) and falls is U-shaped, and few studies have explored the potential mediation of PA accumulation pattern.

Methods

We measured PA in 671 community-dwelling older adults (82.7 ± 3.8 years) using wrist-worn accelerometer for 7 days. PA was further classified to bouted PA (?10 minutes bout length) and sporadic PA (<10 minutes bout length) for subanalysis. Fall incidence in the following 12-month was recorded through tri-monthly telephone interviews. Classification and Regression Tree analysis was used to identify two optimal cutoff values of each PA measurement to predict falls. Participants were then divided into "inactive," "moderately active," and "highly active" groups accordingly. Negative binomial regression models were used to estimate the association between the PA measures and fall incidence.

Results

Six hundred and thirty-nine participants completed 12-month follow-up. Ninety-three (14.6%) experienced a total of 118 falls. Inactive and highly active older adults had higher falls per person month relative to the moderately active group (inactive: incidence rate ratios [IRR] = 2.372, 95% confidence interval [CI] = 1.317-4.271; highly active: IRR = 2.731, 95% CI = 1.196-6.232). Subanalyses found similar significant finding with bouted PA (p < .001) but not sporadic PA (p ? .221). The association between bouted PA and falls remained significant even after adjusting fall incidence for bouted activity time (inactive: IRR = 3.636, 95% CI = 2.238-5.907; highly active: IRR = 1.823, 95% CI = 1.072-3.1). Further adjustments for fall-related risk factors did not meaningfully change the results.

Conclusion

A U-shaped relationship was identified between bouted but not sporadic PA and fall incidence. There is an approximately twofold increase in fall rate in highly active older adults even after adjusting for activity time.

SUBMITTER: Lu Z

PROVIDER: S-EPMC7518560 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study.

Lu Zhihui Z Lam Freddy M H FMH Leung Jason C S JCS Kwok Timothy C Y TCY

The journals of gerontology. Series A, Biological sciences and medical sciences 20200901 10

<h4>Background</h4>It remains uncertain whether the association between physical activity (PA) and falls is U-shaped, and few studies have explored the potential mediation of PA accumulation pattern.<h4>Methods</h4>We measured PA in 671 community-dwelling older adults (82.7 ± 3.8 years) using wrist-worn accelerometer for 7 days. PA was further classified to bouted PA (≥10 minutes bout length) and sporadic PA (<10 minutes bout length) for subanalysis. Fall incidence in the following 12-month was ...[more]

PMID: 32115656

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Project description:Background and objectivesMedication exposure is a potential risk factor for falls and subsequent death and functional decline among older adults. However, controversy remains on the best way to assess medication exposure and which approach best predicts falls. The objective of the current study was to examine the association between different measures of medication exposure and falls risk among community-dwelling older adults.Research design and methodsThis retrospective cohort study was conducted using Falls Free PA program data and a linked prescription claims data from Pennsylvania's Pharmaceutical Assistance Contract for the Elderly program. Participants were community-dwelling older adults living in Pennsylvania, United States. Three measures of medication exposure were assessed: (a) total number of regular medications (polypharmacy); (b) counts of potentially inappropriate medications derived from current prescription guidance tools (Fall Risk-Increasing Drugs [FRIDs], Beers Criteria); and (c) medication burden indices based on pharmacologic mechanisms (Anticholinergic Cognitive Burden, Drug Burden Index) all derived from claims data. The associations between the different medication risk measures and self-reported falls incidence were examined with univariate and multivariable negative binomial regression models to estimate incidence rate ratios (IRRs).ResultsOverall 343 older adults were included and there were 236 months with falls during 2,316 activity-adjusted person-months (10.2 falls per 100 activity-adjusted person-months). Of the 6 measures of medication risk assessed in multivariate models, only the use of 2 or more FRIDs (adjusted IRR 1.67 [95% CI: 1.04-2.68]) independently predicted falls risk. Among the 13 FRID drug classes, the only FRID class associated with an increased fall risk was antidepressants.Discussion and implicationsThe presence of multiple FRIDs in a prescription is an independent risk factor for falls, even in older adults with few medications. Further investigation is required to examine whether deprescribing focused on FRIDs effectively prevents falls among this population.

Project description:BACKGROUND:Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. METHODS:Community-dwelling subjects aged ? 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method. RESULTS:Of the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p<0.001), respectively. The best model that comprised only robotic parameters (the 20 parameters identified in the final model) achieved a better performance than the clinical parameters alone, but worse than the combination of both clinical and robotic variables (ROC AUC: 0.73, 95% CI 0.63-0.83). CONCLUSION:A multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.

Dataset Information

The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study.

Background

Methods

Results

Conclusion

Publications

The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study.

Similar Datasets

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