Figure 5 - uploaded by Yusuf Bhagat
Content may be subject to copyright.
Source publication
Quantification of energy storage is essential in understanding energy balance and can be determined by bioelectrical impedance analysis (BIA). Here, we have developed a smartphone form factor multi-frequency BIA device that incorporates an analog front end for body composition measurements. The device was compared against a reference gel-electrode...
Context in source publication
Context 1
... phase angle given as the arc tangent of the ratio of reactance to resistance can also be derived from multi- frequency BIA devices for probing the health of a tissue across age [ 5]. Here, we used the impedance ratio (50/100 KHz), a surrogate measure of the phase angle and compared it against age in men and women to probe tissue status. All data points falling >2*SD outside their respective predicted values were removed from inclusion in further analyses. III. RESULTS Preliminary results from a sub-sample of the current study population showed strong associations (R 2 0.86) between DXA-based body composition parameters and those estimated from BF-01 across both anatomical pathways [6]. Data from 13 subjects (~4%) was excluded on the basis of outlier analysis criteria. In the remaining subjects, we observed significant (p<0.001) correlations between impedance index derived by the BF meter prototype device with the PP pathway and DXA whole body LST as seen in Fig. 4. The regression model developed for each pathway at 50 KHz was extensible at all higher frequencies ( 100 KHz) examined. When compared against DXA whole body LST, the impedance index derived from the InBody reference BIA system yielded similar correlations to the BF device, albeit slightly larger in magnitude as seen in Fig. 5 for 50 and 250 KHz frequencies. This finding was not surprising owing to the enhanced contact afforded by the gel-based electrodes of the InBody system. The correlation trends seen here provide further support for our mHealth multi-frequency BIA system which holds promise as a means of quantifying body composition in a small form factor without the need for body weight measurement. The impedance ratio (50/100 KHz), an alternative marker for the phase angle trended negatively when correlated against age in men and women (R 2 = 0.11-0.12; Fig. 6). Given that lower phase angles are consistent with low reactance and denote either cell death or selective cell membrane permeability, a decreasing trend in the impedance ratio across age may provide insights into the health of body compartments. IV. CONCLUSION This report showed results from a novel mHealth-based body composition measurement prototype device offering multi-frequency BIA potential in a smartphone form factor. The device incorporates an analog front end with AC rectification and a Bluetooth module for enhanced connectivity and data transfer. Strong associations were noted between DXA-based LST and the impedance index derived at 50 KHz (R 2 = 0.88; p<0.001), the conventional frequency used in several clinical grade and consumer BIA devices. Further, these prototypes were able to generate similar regression models at all higher frequencies (100-200 KHz) also (R 2 = 0.84-0.88) mirroring results from the larger commercial grade gel-electrode based InBody BIA systems. Additionally, the impedance ratio, a surrogate measurement for the phase angle negatively correlated against age providing insights into the health status of tissues beyond just body composition. This could lead to the development of body fat prediction equations independent of subject age and serve as a biomarker of aging. Our current plans involve undertaking a Phase II trial on 100 children and adolescents (10-17 years) with the BF meter device. Such a study will provide insights into childhood obesity which has a high prevalence rate (17%) in the US. In parallel, we are also developing novel multi- frequency BIA AFE integrated circuits for higher frequency applications (up to 1.0 MHz) with low power consumption to rival clinical grade devices. Digital and mobile health innovations permit integration of BIA as an additional physiological sensing modality in small form factor devices for seamless lifestyle incorporation and for achieving user-centric health management. Clinical trials such as the one reported here help validate the multi- frequency devices along with the development of body composition prediction equations across a range of populations and body ...
Similar publications
Background
A commercial three-dimensional optical (3DO) scanning system was reported to be used in body composition assessment. However, the applicability in Chinese adults has yet to be well-studied.
Methods
This secondary analysis was based on a 16-week weight-loss clinical trial with an optional extension to 24 weeks. Waist and hip circumferenc...
[Purpose] This study involved performing longitudinal measurements of muscle mass in elderly patients with mild disequilibrium using a body composition meter. The rate of change and characteristics were determined according to the level of care needed. [Participants and Methods] Bioelectrical impedance was used to measure body composition in 20 eld...
Tennis is a sport characterized by high physical activity and frequently repeated motions, especially for the dominant upper limb. This creates differences between upper limbs and lead to an asymmetric distribution of muscle mass and unbalanced muscle tonus. The aim of the study is to estimate the degree of muscle mass asymmetry between the dominan...
Bioelectric Impedance Vector Analysis (BIVA) can be used to qualitatively compare indi-viduals' hydration and cell mass independently of predictive equations. This study aimed to analyze the efficiency of BIVA considering chronological age and handgrip strength in adolescent athletes. A total of 273 adolescents (male; 59%) engaged in different spor...
Malnutrition is common in haemodialysis (HD) and is linked to poor outcomes. This study aimed to describe changes in body composition after the initiation of HD and investigate whether any routinely collected parameters were associated with these changes. The study cohort came from the HD population of a single centre between 2009 and 2014. Body co...
Citations
... Only one known prior study has explored a wearable bioimpedance device for accuracy in body results of our study reflect the strong agreement found between other portable methods developed to assess body composition (11,35). Prior studies have established the accuracy of 8-BIA compared to DXA in healthy adults across a wide age range as well as including all BMI categories, although the same finding was not observed in our study (22). ...
... We did not explore the variation in the accuracy of results based on incorrectly input height or weight values. We also did not evaluate the accuracy of the provided standard metabolic rate as opposed to basal metabolic rate, though prior work indicates that the accuracy of this estimation is relatively robust (35). The loss of 34 subjects due to completion of a 10-second measurement was also a limitation. ...
Background
Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.
Objectives
This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.
Design
Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (DXA) scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor change in body composition.
Results
Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (p < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA, p > 0.05, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.
Conclusions
After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.
... New advances have been made in traditional anthropometry with the development of automated optical scanning systems, which can quickly provide body dimensions such as length, breadth and circumference 92 and a few validation studies have shown that these optical methods compare well with reference methods, although further refinements of the methods are required. BIA instruments based on smartphone technology are also available 93,94 . Further innovations in sensor-based technologies of body composition are likely to make these techniques more easily and widely used. ...
Body composition is known to be associated with several diseases, such as cardiovascular disease, diabetes, cancers, osteoporosis and osteoarthritis. Body composition measurements are useful in assessing the effectiveness of nutritional interventions and monitoring the changes associated with growth and disease conditions. Changes in body composition occur when there is a mismatch between nutrient intake and requirement. Altered body composition is observed in conditions such as wasting and stunting when the nutritional intake may be inadequate. Overnutrition on the other hand leads to obesity. Many techniques are available for body composition assessment, which range from simple indirect measures to more sophisticated direct volumetric measurements. Some of the methods that are used today include anthropometry, tracer dilution, densitometry, dual-energy X-ray absorptiometry, air displacement plethysmography and bioelectrical impedance analysis. The methods vary in their precision and accuracy. Imaging techniques such as nuclear magnetic resonance imaging and computed tomography have become powerful tools due to their ability of visualizing and quantifying tissues, organs, or constituents such as muscle and adipose tissue. However, these methods are still considered to be research tools due to their cost and complexity of use. This review was aimed to describe the commonly used methods for body composition analysis and provide a brief introduction on the latest techniques available.
... Body composition analysis has not been immune to this possibility. Already smartphone-based BIA measurement systems are available [21,23] and optical scanning of the whole body to provide body volume and anthropometric data using the smartphones camera is in the early stages of development [24]. The use of consumer mobile technologies for body composition analysis opens up many possibilities. ...
Evaluating a questionnaire used to determine coaches perspectives of an athlete monitoring program. This is then followed by examining the range of assessments for hydration, body composition, vertical jump height, and lower body maximal strength and how these may be practically implemented based on available resources.
Background:
The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI.
Objectives:
The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population.
Methods:
Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error.
Results:
There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing.
Conclusions:
Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.
