Comparison of a 2D <scp>iP</scp>ad application and 3D body scanner to air displacement plethysmography for measurement of body fat percentage

Wagner, Dale R.; Castañeda, F. Vallejo; Bohman, B; Sterr, W

doi:10.1111/jhn.12687

Cited by 11 publications

(20 citation statements)

References 20 publications

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“…50 In particular, excess visceral adipose tissue, that is, the accumulation of adipose tissue within the omentum and abdomen and around abdominal organs, is more strongly associated with the development and progression of cardiometabolic diseases in humans than subcutaneous adiposity. 51 Unfortunately, in clinical practice, as with the routine assessment of WC, body fat distribution is rarely evaluated, 52,53 irrespective of the methodologies available, [54][55][56][57] although the majority of the methods are laborious and require expensive technology.…”

Section: Diet As a Driving Force For The Continuum Of Obesity-relatmentioning

confidence: 99%

Diet‐induced rodent models of obesity‐related metabolic disorders—A guide to a translational perspective

et al. 2020

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Diet is a critical element determining human health and diseases, and unbalanced food habits are major risk factors for the development of obesity and related metabolic disorders. Despite technological and pharmacological advances, as well as intensification of awareness campaigns, the prevalence of metabolic disorders worldwide is still increasing. Thus, novel therapeutic approaches with increased efficacy are urgently required, which often depends on cellular and molecular investigations using robust animal models. In the absence of perfect rodent models, those induced by excessive consumption of fat and sugars better replicate the key aspects that are the root causes of human metabolic diseases. However, the results obtained using these models cannot be directly compared, particularly because of the use of different dietary protocols, and animal species and strains, among other confounding factors. This review article revisits diet-induced models of obesity and related metabolic disorders, namely, metabolic syndrome, prediabetes, diabetes and nonalcoholic fatty liver disease. A critical analysis focused on the main pathophysiological features of rodent models, as opposed to the criteria defined for humans, is provided as a practical guide with a translational perspective for the establishment of animal models of obesity-related metabolic diseases.

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Section: Diet As a Driving Force For The Continuum Of Obesity-relatmentioning

confidence: 99%

Diet‐induced rodent models of obesity‐related metabolic disorders—A guide to a translational perspective

et al. 2020

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“…Air displacement plethysmography (Bod Pod), is a technique that rapidly estimates body fat percentage and is useful for assessing minor differences in body composition in overweight and obese individuals [ 63 ]. However, a recent study questioned the accuracy of the Bod Pod’s estimates of body fat percentage [ 64 ].…”

Section: Techniques For Assessing Obesitymentioning

confidence: 99%

Obesity and Brain Function: The Brain–Body Crosstalk

Sui

Pasco

2020

Medicina

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Dementia comprises a wide range of progressive and acquired neurocognitive disorders. Obesity, defined as excessive body fat tissue, is a common health issue world-wide and a risk factor for dementia. The adverse effects of obesity on the brain and the central nervous system have been the subject of considerable research. The aim of this review is to explore the available evidence in the field of body–brain crosstalk focusing on obesity and brain function, to identify the major research measurements and methodologies used in the field, to discuss the potential risk factors and biological mechanisms, and to identify the research gap as a precursor to systematic reviews and empirical studies in more focused topics related to the obesity–brain relationship. To conclude, obesity appears to be associated with reduced brain function. However, obesity is a complex health condition, while the human brain is the most complicated organ, so research in this area is difficult. Inconsistency in definitions and measurement techniques detract from the literature on brain–body relationships. Advanced techniques developed in recent years are capable of improving investigations of this relationship.

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“…For example, there is some inconsistency in the use of software apps to measure heart rate [8], particularly during exercise of various intensities [9]. Furthermore, a recent review by Peart et al [10] highlighted inconsistencies in validity and reliability when estimating body fat percentage using a range of commercially available software [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Smartphone and Tablet Software Apps to Collect Data in Sport and Exercise Settings: Cross-sectional International Survey

Shaw¹,

Satchell²,

Thompson³

et al. 2021

JMIR Mhealth Uhealth

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Background Advances in smartphone technology have facilitated an increase in the number of commercially available smartphone and tablet apps that enable the collection of physiological and biomechanical variables typically monitored in sport and exercise settings. Currently, it is not fully understood whether individuals collect data using mobile devices and tablets, independent of additional hardware, in their practice. Objective This study aims to explore the use of smartphone and tablet software apps to collect data by individuals working in various sport and exercise settings, such as sports coaching, strength and conditioning, and personal training. Methods A total of 335 practitioners completed an electronic questionnaire that surveyed their current training practices, with a focus on 2 areas: type of data collection and perceptions of reliability and validity regarding app use. An 18-item questionnaire, using a 5-point Likert scale, evaluated the perception of app use. Results A total of 204 respondents reported using apps to directly collect data, with most of them (196/335, 58.5%) collecting biomechanical data, and 41.2% (138/335) respondents reported using at least one evidence-based app. A binomial general linear model determined that evidence accessibility (β=.35, 95% CI 0.04-0.67; P=.03) was significantly related to evidence-based app use. Age (β=−.03, 95% CI −0.06 to 0.00; P=.03) had a significant negative effect on evidence-based app use. Conclusions This study demonstrates that practitioners show a greater preference for using smartphones and tablet devices to collect biomechanical data such as sprint velocity and jump performance variables. When it is easier to access information on the quality of apps, practitioners are more likely to use evidence-based apps. App developers should seek independent research to validate their apps. In addition, app developers should seek to provide clear signposting to the scientific support of their software in alternative ways.

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Comparison of a 2D iPad application and 3D body scanner to air displacement plethysmography for measurement of body fat percentage

Cited by 11 publications

References 20 publications

Diet‐induced rodent models of obesity‐related metabolic disorders—A guide to a translational perspective

Diet‐induced rodent models of obesity‐related metabolic disorders—A guide to a translational perspective

Obesity and Brain Function: The Brain–Body Crosstalk

Smartphone and Tablet Software Apps to Collect Data in Sport and Exercise Settings: Cross-sectional International Survey

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