Do Changes in Mental Energy and Fatigue Impact Functional Assessments Associated with Fall Risks? An Exploratory Study Using Machine Learning.
Using a crossover-design, we assessed changes in 30-second chair stand test (30 s-CST), Timed Up-and-Go (TUG) and Berg Balance Scale (BBS) and energy and fatigue in older adults (N = 11) after performance of mental tasks. A Wilcoxon Sign Rank Test and a Friedman's rank test were used to assess chang...
| Publicado en: | Physical & Occupational Therapy in Geriatrics Vol. 38; no. 3; pp. 283 - 302 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Taylor & Francis Ltd
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144476512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144476512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02703181 PTG jtl: Physical & Occupational Therapy in Geriatrics issn: 02703181 maglogo: N pubinfo: dt: Sep2020 vid: 38 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 144476512 144476512 145561162 144476512 10.1080/02703181.2020.1748788 144476512 ppf: 283 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Do Changes in Mental Energy and Fatigue Impact Functional Assessments Associated with Fall Risks? An Exploratory Study Using Machine Learning. aug: au: Boolani, Ali Ryan, Jenna Vo, Trang Wong, Brandon Banerjee, Natasha Kholgade Banerjee, Sean Fulk, George Smith, Matthew Lee Martin, Rebecca affil: Department of Physical Therapy, Clarkson University, Potsdam, New York, USA sug: subj: Affect Evaluation Mental Fatigue Geriatric Functional Assessment Accidental Falls Risk Factors Accidental Falls Risk Factors Risk Assessment Methods Psychomotor Performance Human Male Female Middle Age Aged Exploratory Research Crossover Design Machine Learning Clinical Assessment Tools Wilcoxon Signed Rank Test Friedman Test Support Vector Machine Algorithms Descriptive Statistics Random Forest Sensitivity and Specificity Randomized Controlled Trials Placebos Random Assignment Coefficient alpha Scales Test-Retest Reliability Neuropsychological Tests Nonparametric Statistics Data Analysis Software Post Hoc Analysis Power Analysis Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Using a crossover-design, we assessed changes in 30-second chair stand test (30 s-CST), Timed Up-and-Go (TUG) and Berg Balance Scale (BBS) and energy and fatigue in older adults (N = 11) after performance of mental tasks. A Wilcoxon Sign Rank Test and a Friedman's rank test were used to assess changes in 30 s-CST, TUG, BBS and energy and fatigue respectively. A linear mixed model was used to assess joint variance and random forest classifier and support vector machine (SVM) algorithms were used to verify results. Statistically significant declines in feelings of energy (p=.003), specifically mental energy (p=.015), and BBS (p<.001), specifically during the "standing with eyes closed" (SEC), was noted for participants on days when they completed mental tasks compared to days they did not. The random-forest and SVM algorithms predicted with 79% and 80% accuracy respectively whether the SEC item of the BBS was performed after a decline a mental energy. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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