Assessment of potential clinical cascade between oral hypofunction and physical frailty: Covariance structure analysis in a cross‐sectional study

2019 ◽  
Vol 47 (1) ◽  
pp. 61-66
Author(s):  
Tsukasa Hihara ◽  
Takaharu Goto ◽  
Tetsuo Ichikawa
Keyword(s):  
Structure Analysis ◽  
Covariance Structure ◽  
Cross Sectional Study ◽  
Covariance Structure Analysis ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional

scholarly journals Associations between lifestyle factors and multidimensional frailty: a cross-sectional study among community-dwelling older people

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Marcel A. L. M. van Assen ◽  
Judith H. M. Helmink ◽  
Robbert J. J. Gobbens
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Cross Sectional Study ◽  
Lifestyle Factors ◽  
Community Dwelling ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Community Dwelling Older People

Abstract Background Multidimensional frailty, including physical, psychological, and social components, is associated to disability, lower quality of life, increased healthcare utilization, and mortality. In order to prevent or delay frailty, more knowledge of its determinants is necessary; one of these determinants is lifestyle. The aim of this study is to determine the association between lifestyle factors smoking, alcohol use, nutrition, physical activity, and multidimensional frailty. Methods This cross-sectional study was conducted in two samples comprising in total 45,336 Dutch community-dwelling individuals aged 65 years or older. These samples completed a questionnaire including questions about smoking, alcohol use, physical activity, sociodemographic factors (both samples), and nutrition (one sample). Multidimensional frailty was assessed with the Tilburg Frailty Indicator (TFI). Results Higher alcohol consumption, physical activity, healthy nutrition, and less smoking were associated with less total, physical, psychological and social frailty after controlling for effects of other lifestyle factors and sociodemographic characteristics of the participants (age, gender, marital status, education, income). Effects of physical activity on total and physical frailty were up to considerable, whereas the effects of other lifestyle factors on frailty were small. Conclusions The four lifestyle factors were not only associated with physical frailty but also with psychological and social frailty. The different associations of frailty domains with lifestyle factors emphasize the importance of assessing frailty broadly and thus to pay attention to the multidimensional nature of this concept. The findings offer healthcare professionals starting points for interventions with the purpose to prevent or delay the onset of frailty, so community-dwelling older people have the possibility to aging in place accompanied by a good quality of life.

scholarly journals Prevalence of frailty in older adults in outpatient physiotherapy in an urban region in the western part of Germany: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027768 ◽  
Author(s):  
Tobias Braun ◽  
Christian Thiel ◽  
Carina Ziller ◽  
Julia Rasche ◽  
Carolin Bahns ◽  
...  
Keyword(s):  
Older Adults ◽  
Frailty Index ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Urban Region ◽  
Sectional Study ◽  
Physical Frailty ◽  
Frailty Phenotype ◽  
Cross Sectional ◽  
Age Related

ObjectiveTo investigate the prevalence of frailty in older people in outpatient physiotherapy services in an urban region in the western part of Germany.DesignCross-sectional study.SettingOutpatient physiotherapy clinics were recruited in the municipal area of the city of Bochum, Germany, and selected randomly.ParticipantsOlder adults aged 65 years and older seeking outpatient physiotherapy.Primary and secondary outcome measuresPrevalence of frailty was assessed based on the frailty phenotype model of physical frailty and the accumulation of deficit model, expressed as a Frailty Index. Prevalence was calculated for the whole sample and according to age-related, sex-related and diagnosis-related subgroups.ResultsA total of 258 participants (74±6 years, 62% female) from 11 out of 130 (8%) different physiotherapy clinics were included. Participants’ main indication for physiotherapy was an orthopaedic or surgical condition (75%). According to the model of a physical frailty phenotype, 17.8% (95% CI 13.2 to 22.5) participants were frail and 43.4% (95% CI 37.4 to 49.5) were prefrail. The Frailty Index identified 31.0% (95% CI 25.4 to 36.7) of individuals as frail. In both models, prevalence increased with age and was higher in women than in men. Slow gait speed (34%), reduced muscle strength (34%) and exhaustion (28%) were the most prevalent indicators of physical frailty.ConclusionsFrailty is comparatively common in older patients attending physiotherapy care in Germany, with one out of three individuals being frail and every second individual being physically frail or prefrail.Trial registration numberDRKS00009384; Results.

scholarly journals Association between physical frailty and cognitive scores in older adults

Rev Rene ◽  
2015 ◽  
Vol 16 (3) ◽  
Author(s):  
Clóris Regina Blanski Grden ◽  
Maynara Fernanda Carvalho Barreto ◽  
Jacy Aurélia Vieira de Sousa ◽  
Juliana Andrade Chuertniek ◽  
Péricles Martim Reche ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Assessment ◽  
Mini Mental State Examination ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Convenience Sample ◽  
Care Plans ◽  
State Examination

Objective: to investigate the association between physical frailty and cognitive scores in older adults at an Open Universityof the Third Age in Southern Brazil. Methods: descriptive cross-sectional study with convenience sample comprising 100elderly, conducted from March to June 2013. For cognitive assessment, we applied the Mini Mental State Examination andthe Edmonton Frail Scale. Results: there was a predominance of females (93%), with a mean age of 65.6 years. 81% ofthe participants were classified as non-frail, 16% as apparently vulnerable to frailty, and 3% as mild frailty. There was asignificant association between cognitive performance and frailty (p<0.006). Conclusion: the research on the associationbetween physical frailty and cognitive scores in older people promotes the construction of gerontological care plans aimedat managing this syndrome.

scholarly journals Physical frailty and fitness of older driver

2017 ◽  
pp. 41-46 ◽  
Author(s):  
Maria Helena Lenardt ◽  
Nathalia Hammerschmidt Kolb Carneiro ◽  
Tânia Maria Lourenço ◽  
Clovis Cechinel ◽  
Maria Angelica Binotto
Keyword(s):  
Cross Sectional Study ◽  
Descriptive Statistics ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Older Driver ◽  
Car Accidents ◽  
Driving Fitness ◽  
The City ◽  
Structured Questionnaire

Aim: to analyze the association between physical frailty and the results of fitness capacity exams for driving vehicles among elder Brazilians. Methods: this is a cross sectional study, performed in traffic medicine clinics of the city of Curitiba (Brazil). The data was collected through the physical frailty tests, the use of a structured questionnaire, and searches on the records of the Brazilian National Register of Qualified Drivers.To analyze the information, the authors used descriptive statistics and non-parametrical tests. Results: One hundred seventy two elderly people, of whom 56.4% pre-fragile and 43.6% non-fragile. 25.0% were considered fit for driving, 68.6% were considered fit, but with some restrictions, and 6.4% were placed as temporarily unfit for driving. There was no association between frailty condition and the final results for driving fitness (p= 0.8934). Physical frailty was significantly associated to the restrictions observed for those who were fit under restrictions (p= 0.0313), according to the weekly amount of kilometers traveled (p= 0.0222), and to car accidents occurred after the age of 60 (p= 0.0165). Conclusion: Physical frailty was significantly associated to the restrictions related to driving, reason to which makes important to manage frailty over this group of drivers. However, no association observed between physical frailty and the final result for driving vehicles.

Beginselen van covariantie-structuuranalyse

2007 ◽  
Vol 20 (1) ◽  
Author(s):  
Herbert J.A. Hoijtink ◽  
Jan de Jonge
Keyword(s):  
Structure Analysis ◽  
Covariance Structure ◽  
Structural Models ◽  
Control Model ◽  
Covariance Structure Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Measurement Models ◽  
Means Of Measurement ◽  
Demand Control

Principles of covariance structure analysis Principles of covariance structure analysis H.J.A. Hoijtink & J. de Jonge, Gedrag & Organisatie, volume 20, maart 2007, nr. 1, pp. 57-81 This paper is an introduction to covariance structure analysis (CSA). The ins and outs of this technique are explained by means of measurement models and structural models, using the Demand-Control Model of Karasek (1979) as a theoretical and empirical illustration. LISREL, Mplus, EQS and Amos are useful and accessible CSA-software. This paper explains the key evaluation points of CSA for the practical researcher. The comparison of competitive models, the use of several fit measures, and capitalization on chance was explained by means of cross-sectional survey data from two existing research studies. The article concludes with several critical notes as to using CSA, as well as with suggestions for literature concerning CSA.

scholarly journals Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

10.2196/32724 ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. e32724
Author(s):  
Moritz Kraus ◽  
Maximilian Michael Saller ◽  
Sebastian Felix Baumbach ◽  
Carl Neuerburg ◽  
Ulla Cordula Stumpf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gait Analysis ◽  
Physical Performance ◽  
Learning Algorithms ◽  
Cross Sectional Study ◽  
Machine Learning Algorithms ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Gait Parameters

Background Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

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