Relation Between Respiratory Muscle Strength and Skeletal Muscle Mass and Hand Grip Strength in the Healthy Elderly

Shin, Hyung-Ik; Kim, Don Kyu; Seo, Kyung Mook; Kang, Si Hyun; Lee, Sang Yoon; Son, Sunhan

doi:10.5535/arm.2017.41.4.686

Cited by 76 publications

(66 citation statements)

References 33 publications

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“…Sarcopenia has a complex multifactorial pathogenesis, which involves lifestyle habits (i.e., malnutrition, physical inactivity), disease triggers, and age-dependent biological changes (i.e., chronic inflammation, mitochondrial abnormalities, loss of neuromuscular junctions, reduced satellite cell numbers, hormonal alterations) [108,109]. Sarcopenia is a whole-body process, affecting not only lower extremities, but also muscles dedicated to breathing, mastication, and swallowing [14,[18][19][20][21][22]. In particular, swallowing is a complex mechanism involving several head and neck muscles simultaneously and in conjunction to coordinate the entire process [110].…”

Section: Sarcopenia and Oral Statusmentioning

confidence: 99%

“…Interestingly, in recent years, it has been highlighted that sarcopenia is not limited to lower limbs, but is a whole-body process [14][15][16], also affecting the muscles devoted to chewing and swallowing [10,17], with a negative impact on food intake. In fact, atrophy of muscles critical for the respiratory and swallowing functions has been reported [14,[18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Poor Oral Health as a Determinant of Malnutrition and Sarcopenia

et al. 2019

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Aging is accompanied by profound changes in many physiological functions, leading to a decreased ability to cope with stressors. Many changes are subtle, but can negatively affect nutrient intake, leading to overt malnutrition. Poor oral health may affect food selection and nutrient intake, leading to malnutrition and, consequently, to frailty and sarcopenia. On the other hand, it has been highlighted that sarcopenia is a whole-body process also affecting muscles dedicated to chewing and swallowing. Hence, muscle decline of these muscle groups may also have a negative impact on nutrient intake, increasing the risk for malnutrition. The interplay between oral diseases and malnutrition with frailty and sarcopenia may be explained through biological and environmental factors that are linked to the common burden of inflammation and oxidative stress. The presence of oral problems, alone or in combination with sarcopenia, may thus represent the biological substratum of the disabling cascade experienced by many frail individuals. A multimodal and multidisciplinary approach, including personalized dietary counselling and oral health care, may thus be helpful to better manage the complexity of older people. Furthermore, preventive strategies applied throughout the lifetime could help to preserve both oral and muscle function later in life. Here, we provide an overview on the relevance of poor oral health as a determinant of malnutrition and sarcopenia.

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Section: Sarcopenia and Oral Statusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Poor Oral Health as a Determinant of Malnutrition and Sarcopenia

et al. 2019

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“…The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.Sensors 2020, 20, 70 2 of 27 during pregnancy and lactation [6], in the evaluation of the risk of various pathologies [7,8], as a marker or direct cause of disease [9,10], during the process of decision making in an illness [11], aging [12] or rehabilitation processes [13], as a complement to the diagnosis and follow-up of conditions related to the cardiovascular system [14,15], in oncology [16] and even in sports science [17], among others.Despite the advances in the clinical application of bioimpedance, there are still some challenges to be solved, such as the integration of devices into e-Health systems supporting remote user monitoring [18]. In addition, the complexity of bioimpedance systems is usually quite high (current injection, voltage measurement, demodulation, processing, etc.)…”

mentioning

confidence: 99%

“…Sensors 2020, 20, 70 2 of 27 during pregnancy and lactation [6], in the evaluation of the risk of various pathologies [7,8], as a marker or direct cause of disease [9,10], during the process of decision making in an illness [11], aging [12] or rehabilitation processes [13], as a complement to the diagnosis and follow-up of conditions related to the cardiovascular system [14,15], in oncology [16] and even in sports science [17], among others.…”

mentioning

confidence: 99%

“…The error in the estimation of the model is reduced if the number of measurement frequencies is increased [57], because the model that provides a best fit to all frequencies provides a safeguard against noise at any frequency. For this reason, a high number of frequencies has been used in the two studies carried out: 20 points used in the hardware validation study described in Section 3.1 and 50 for the software validation study described in Section 3.2 (all frequencies are in kHz): 5,6,7,8,9,10,11,12,13,14,15,16,18,20,23,25,28,31,35,39,43,50,54,60,67,75,83,93,100,115,128,143,159,177,200,220,245,273,304,339,378,421,469, 500, 582, 649, 723, 806, 898, 1000. The points that were only used in the hardware validation study are underlined.…”

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confidence: 99%

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Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

Naranjo-Hernández

Reina‐Tosina

Roa

et al. 2019

Sensors

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The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.Sensors 2020, 20, 70 2 of 27 during pregnancy and lactation [6], in the evaluation of the risk of various pathologies [7,8], as a marker or direct cause of disease [9,10], during the process of decision making in an illness [11], aging [12] or rehabilitation processes [13], as a complement to the diagnosis and follow-up of conditions related to the cardiovascular system [14,15], in oncology [16] and even in sports science [17], among others.Despite the advances in the clinical application of bioimpedance, there are still some challenges to be solved, such as the integration of devices into e-Health systems supporting remote user monitoring [18]. In addition, the complexity of bioimpedance systems is usually quite high (current injection, voltage measurement, demodulation, processing, etc.) and the use of high-frequency signals (tens to hundreds of kHz) generally requires high power consumption, so new challenges arise for the optimization of hardware in size, energy efficiency, robustness and precision [1,19]. On the other hand, although the bioimpedance spectroscopy technique has proven to be more precise and robust than the single frequency technique, most devices base their operation on the measurement on a single frequency [1,20].Other issues are related to the models and algorithms used to estimate the BC from bioimpedance measures. A key point for an adequate BC estimation by bioimpedance analysis is a correct parameter identification of the bioimpedance model, usually the Cole model [1,[21][22][23][24][25][26][27][28][29][30][31]. Sometimes, this parameterization is performed without directly measuring the impedance values [21][22][23]. Other authors use...

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