A comparison between two methods for measuring limb resistances with wrist and ankle electrodes

Accuracy of Smart Scales on Weight and Body Composition: Observational Study

Background

Smart scales are increasingly used at home by patients to monitor their body weight and body composition, but scale accuracy has not often been documented.

Objective

The goal of the research was to determine the accuracy of 3 commercially available smart scales for weight and body composition compared with dual x-ray absorptiometry (DEXA) as the gold standard.

Methods

We designed a cross-sectional study in consecutive patients evaluated for DEXA in a physiology unit in a tertiary hospital in France. There were no exclusion criteria except patient declining to participate. Patients were weighed with one smart scale immediately after DEXA. Three scales were compared (scale 1: Body Partner [Téfal], scale 2: DietPack [Terraillon], and scale 3: Body Cardio [Nokia Withings]). We determined absolute error between the gold standard values obtained from DEXA and the smart scales for body mass, fat mass, and lean mass.

Results

The sample for analysis included 53, 52, and 48 patients for each of the 3 tested smart scales, respectively. The median absolute error for body weight was 0.3 kg (interquartile range [IQR] –0.1, 0.7), 0 kg (IQR –0.4, 0.3), and 0.25 kg (IQR –0.10, 0.52), respectively. For fat mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.7 kg (IQR –8.0, 0.28), respectively. For muscular mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.65 kg (IQR –8.03, 0.28), respectively. Factors associated with fat mass measurement error were weight for scales 1 and 2 (P=.03 and P<.001, respectively), BMI for scales 1 and 2 (P=.034 and P<.001, respectively), body fat for scale 1 (P<.001), and muscular and bone mass for scale 2 (P<.001 for both). Factors associated with muscular mass error were weight and BMI for scale 1 (P<.001 and P=.004, respectively), body fat for scales 1 and 2 (P<.001 for both), and muscular and bone mass for scale 2 (P<.001 and P=.002, respectively).

Conclusions

Smart scales are not accurate for body composition and should not replace DEXA in patient care.

Trial Registration

ClinicalTrials.gov NCT03803098; https://clinicaltrials.gov/ct2/show/NCT03803098

The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases

Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.

Segmental bioelectrical impedance analysis: an update

PURPOSE OF REVIEW

Bioelectrical impedance analysis is a popular, noninvasive and practical method for assessment of body composition. The last decade has seen the development of impedance analyzers designed to assess the composition of body segments as well as the whole body. This review outlines the theoretical basis for segmental impedance analysis, validity and use in practice.

RECENT FINDINGS

Segmental impedance analysis tends to underestimate fat-free mass and overestimate fat mass when compared to reference techniques, although the magnitude of these differences can be small. Performance is improved with population-specific prediction equations; algorithms in-built into instrument firmware should not be relied upon. Prediction of whole-body composition from the sum of the individual segments, although theoretically preferable, shows little advantage over whole body wrist to ankle impedance approaches. Prediction of appendicular skeletal muscle mass, although promising, requires further research. The use of measured impedance data directly as indices of composition, rather than for prediction, has not found extensive application in nutritional research despite its success in other fields.

SUMMARY

Segmental bioimpedance techniques have advanced substantially in recent years due to availability of simple-to-use analyzers and simplified measurement protocols. The method has been well validated and increasingly adopted in nutritional and clinical practice. Segmental impedance, like conventional whole body impedance approaches, provides indirect prediction of body composition whose accuracy is yet to achieve that of reference techniques such as magnetic reference imaging. This lack of accuracy, however, is outweighed by the method's practicality of use in many settings.