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BioImpedance Analysis (BIA) is a safe, simple, and noninvasive technology to measure body composition. By measuring the electrical impedance of biological tissues, BIA provides valuable biological insights such as body composition, hydration status, and some health conditions. The principle is to apply an electric current to body segments, which water content and conductivity are characteristics, and to determine the electric impedance depending on body tissues passed through. However, these measurements are indirectly related to body composition and intensively depend on limited and imprecise assumptions to estimate mathematical models. This is the source of methodological and experimental challenges. BIA is very promising to offer non-invasive and portable solutions to assess health status and well-being, but challenges must be considered: they impact technological limitations, methodological standardization, and data interpretation. Advancements in BIA require to address these hurdles to improve accuracy, reliability, and applicability in diverse settings. In this article, we reviewed in depth these challenges based on a systematic review of literature. The objective of this systematic review is to identify key challenges of BIA to assess body composition to develop possible directions for improving this technology. Our review underlines clearly the need to reduce these challenges with the multiplication of biostatistical sources, the definition of personalized models, and the adjustment of mathematical assumptions, to improve BIA reliability and adoption in e-health or specific applications. The objective of this systematic review from published literature was to answer the question: "How to assess whole body composition in the average human adult with BIA, what are the scientific challenges and limits for a wider adoption in medical practice?". We limited our research within Pubmed, ScienceDirect and IEEE complementary databases. Our research was carried out in English using the keywords "body composition" and "bioimpedance analysis" over a period from the included 1995 to 2022. We controlled inclusion criteria to collect only articles with average human adults' groups: age from 18 years, both males and females, mixed ethnics, BMI ranging from 18 to 30 kg/m2, either healthy or non-healthy status. We added the following exclusion criteria: athletics, malnourished, eating or mental disorders, pregnancy and menstrual period. Finally, we kept articles validated versus state-of-the-art methods DEXA, or isotope dilution. Our literature review identified seven major challenges with BIA: Rheological modeling precision represent human body as an electrical circuit made of resistors and capacitors to reflect electrical properties of tissues; Body compartments to model human body as a combination of cylinders different tissues type and fluids volumes; Physiological approximations as anthropometric data used in body composition modeling refer to an ancient population from 1975 (ethnicity: Caucasian, body mass index: BMI=24, sex: male, height: 170 cm, age: 25 years, health status: healthy...); Predefined constants to predict body composition were calculated on healthy subjects; Electrical stimulation frequency choice as the impedance depends on the value and the number of frequencies used for the measure; Flow of current inside the body may not be uniform nor following the same pathway crossing all body tissues and finally Standardization ofmeasurement protocols and body position to minimize the interferences and factors affecting the accuracy of BIA measurement. BIA is simple, easy to use, and noninvasive technique integrated in portable, wearable, and connected health solutions. The complexity of rheological models cannot reflect precisely the complexity of the human body. The compartment numbers considered for tissues modelling is critical for results accuracy, the commonly used configurations are the 3-C and 5-C to predict body composition referring to standard methods. Numerous physiological assumptions introduce several factors of variability that must not be generalized, the assumptions should be applied on groups with similar characteristics as the population studied only and must include subjects specificities. The models assume the use of constant values that are generic, imprecise, and estimated on limited healthy groups, future work needs to customize population-specific equations. Multiplication of electrical stimulations at different frequencies is required to consider different types of tissues and to guarantee a response from all tissues. The measures are significantly influenced by electrodes positioning, gel and dry electrodes both imply trade-offs between accuracy, convenience, and mobility. There is no one-size-fits-all answer, nevertheless standardization of procedures is a step for BIA studies to move forward and subsequently improve accuracy and reduce the gaps when results from different devices are compared. From this review, it looks critical to improve BIA methods by developing novel electrodes designs that may improve electrical contact and reduce contact impedance or by exploring the use of smart textiles and wearable electrodes for continuous monitoring of body composition and hydration status. Acquiring more data, at several electrical stimulation frequencies and in different contexts (healthy and pathological status, ethnicities, ages, comorbidities...) to enrich references and adjust constant values. Analyzing large datasets to refine prediction models. These improvements are essential prerequisites so incorporation of machine learning and artificial intelligence algorithms can explore individual variability in the future and improve the potential benefits of BIA predictions in research and clinical practice.
Dimassi et al. (Sat,) studied this question.