Comparative analysis of body parameters for fracture and post-healing patient using a non-invasive bioimpedance device

Abstract

Bone fracture assessment and follow-up commonly rely on imaging techniques such as X-ray and CT, which, although effective, involve radiation exposure and limited feasibility for frequent monitoring. This study presents a comparative analysis of body impedance parameters in fractured and non-fractured individuals to explore the potential of bio-impedance as a non-invasive diagnostic and monitoring tool. Data were collected from 65 patients during the fracture phase and after complete healing, allowing direct comparison of electrical characteristics associated with tissue recovery. The study focused on key parameters such as impedance magnitude, and phase angle to assess their variation with bone healing. A portable bio-impedance measurement device was developed to acquire real-time data with high accuracy and reproducibility. The results indicate consistent within-subject trends, with higher impedance and phase angle values observed in the post-healing condition. These findings suggest that segmental BIA measurements may reflect changes associated with fracture recovery; however, no causal physiological mechanisms are inferred. The developed system proved effective in capturing these physiological transitions without the need for invasive or radiological procedures. This work establishes bio-impedance analysis as a promising, safe, and cost-effective approach for monitoring bone healing and assessing fracture status. The proposed device can serve as a valuable clinical tool for orthopedic applications, enabling early detection of healing progress and personalized rehabilitation strategies.

Introduction

Fractures represent one of the most common forms of musculoskeletal injury and are a major cause of morbidity worldwide¹. While fractures primarily affect the structural integrity of bones, their consequences extend far beyond the site of injury, influencing systemic physiology and overall body composition². The period of fracture and immobilization is often associated with reduced physical activity, altered metabolism, muscle atrophy, and fluid imbalance, all of which can result in significant changes in anthropometric and body composition parameters³. Understanding these changes is crucial because they not only provide insights into the impact of trauma on the human body but also serve as an important reference for planning effective rehabilitation strategies⁴. Evaluating how the body responds during the fractured state and how it recovers after healing offers a deeper perspective into the trajectory of physiological adaptation, thereby supporting clinical monitoring and long-term patient care⁵.

Fracture injury patterns resulting from falls, particularly high falls, have been extensively examined in forensic and clinical research to understand the biomechanical and physiological consequences associated with different fall heights and patient characteristics. In⁶ author has demonstrated that the severity and distribution of injuries following fatal falls can be mathematically correlated with the height of impact, highlighting predictable patterns of tissue damage across varying fall dynamics. Similarly⁷, reported that factors such as sex, BMI, and fall height significantly influence the extent of trauma sustained during fatal falls, suggesting that individual physiological attributes may shape the injury profile of fracture. Autopsy based investigations further support these relationships, as shown by Ramadan et al., who documented distinct injury distributions in fall-from-height fatalities within the Gharbia Governorate, reinforcing the role of fall mechanism and impact conditions in determining injury severity⁸. Comparative studies have also emphasized notable differences between suicidal and accidental high falls, with Tsellou et al. identifying characteristic variations in injury severity and anatomical distribution between the two categories⁹. Additional work by¹⁰ confirmed that the level of the fall is strongly associated with the extent and pattern of trauma, providing further evidence of height-dependent physiological disruption. Recent medicolegal analyses from Tunisia by¹¹. similarly reported consistent correlations between fall height and fracture injury severity across 141 autopsy cases, underscoring the profound systemic impact of high-energy trauma¹¹. Collectively, these studies highlight how external mechanical forces, such as those involved in falls, can cause substantial alterations in tissue integrity and physiological responses underscoring the importance of objective diagnostic tools, including bioimpedance analysis, for evaluating the internal effects of musculoskeletal injuries.

Anthropometric indices such as body weight, height, and body mass index (BMI) are fundamental markers of nutritional and health status, while advanced techniques such as bioelectrical impedance analysis (BIA) provide non-invasive estimation of body composition, including body fat, fat-free mass (FFM), and total body water (TBW)¹². These parameters are highly responsive to physiological stressors: for example, immobilization can reduce FFM due to loss of muscle activity, whereas inflammation and limited mobility may alter fluid distribution and TBW¹³. During fracture recovery, nutritional intake, physiotherapy, and restored mobility are expected to normalize these values, reflecting improved systemic balance¹⁴. Monitoring such parameters in a longitudinal manner tracking the same patients across fractured and healed states provides more reliable evidence than cross-sectional comparisons, as individual variability is minimized and true changes attributable to fracture and healing can be identified¹⁵.

Previous studies have often compared body composition between patients and healthy controls, but relatively few have explored the same individuals during fracture and after complete recovery². A within-subject analysis allows for more precise detection of physiological changes, as each individual serves as their own control¹⁶. This design helps to eliminate confounding factors such as genetic makeup, lifestyle, or baseline anthropometry, which often complicate case control studies¹⁷. By examining how body parameters shift between fractured and healed states, it becomes possible to quantify the exact influence of fractures on body composition and evaluate the degree of recovery after healing^18–20.

In this study, 65 patients with clinically diagnosed fractures were assessed twice: first during their fractured state and subsequently after complete clinical healing. Anthropometric and body composition parameters, including body weight, BMI, body fat, FFM, and TBW, were measured at both stages using standardized methods. The collected data were analyzed to determine significant differences within the same subjects across the two conditions. The findings of this longitudinal study are expected to provide valuable insights into the physiological effects of fractures, highlight the importance of body parameter assessment in rehabilitation, and contribute to the development of more effective monitoring strategies for patient recovery.

Limitations

The present study has some limitations that should be acknowledged to aid in the interpretation of the findings. First, the analysis lacked stratification based on critical injury-related variables, including fracture location (upper vs. lower limb), type of fracture (open vs. closed), and severity of tissue damage. Second, key clinical details such as exact injury location, mechanism of injury, and immobilization period were not collected at the time of data acquisition. The absence of these parameters limits the ability to correlate bioimpedance changes with specific injury characteristics. Third, physical activity level and rehabilitation status of the patients, which can significantly impact fluid distribution and tissue composition, were not controlled in the present study. Moreover, the time since injury was not uniformly documented, although bioimpedance measurements are highly time-dependent during the healing process. Together, these constraints restrict the generalizability and clinical interpretation of the results.

Organization of the paper

This paper is organized into five main sections to provide a comprehensive understanding of the developed BIA system and its application in monitoring fracture healing. Section 2 describes the design, architecture, and validation of the single-frequency segmental bio-impedance device, including hardware components, signal acquisition, and calibration procedures. Section 3 explains the methodology adopted for patient recruitment, ethical approval, measurement protocols, and data collection during both fracture and post-healing stages. Section 4 presents the results obtained from anthropometric and bioimpedance measurements. Section 5 provides the overall conclusion of the study.

BIA device development and validation

In this section BIA device designing and its validation has been explained in detail.

Single frequency body segment BIA device designing

The proposed bioelectrical impedance measurement system, illustrated in Fig. 1, integrates multiple functional blocks to achieve accurate and real-time monitoring of segmental body impedance. A regulated power supply initiates the operation by delivering stable voltage to all modules. At the core, the AD5933 impedance converter chip generates a sinusoidal excitation signal and measures the resulting voltage drop across biological tissue using a four-electrode Ag/AgCl configuration. As shown in Fig. 1, the acquired data, comprising both real and imaginary components of impedance, is communicated to the ESP32-WROOM module through the I²C interface. Acting as the system’s central controller, the ESP32-WROOM performs data processing, calibration, and wireless transmission. Measurement parameters such as impedance, phase angle, and frequency sweep results are displayed in real time on a 20 × 4 LCD. Additionally, the ESP32 transmits processed information to a remote server via Wi-Fi, facilitating continuous data storage and enabling remote accessibility for advanced analysis or clinical evaluation. This compact and cost-effective design ensures efficient signal acquisition, local visualization, and remote health monitoring capabilities.

Fig. 1.

Block diagram of developed body segmental BIA device.

Table 1 summarizes the essential components and specifications of the proposed BIA device, which is developed around the AD5933 Evaluation Board serving as the primary impedance converter. The AD5933 is a high-precision integrated circuit that combines a frequency generator and a 12-bit ADC, enabling impedance measurements up to 50 kHz via an I²C interface. The ESP32-WROOM microcontroller functions as the central processing unit, offering dual-core performance at 240 MHz along with built-in Wi-Fi, Bluetooth, and multiple communication interfaces such as UART, SPI, and I²C. Measurement results are displayed on a 20 × 2 LCD, with contrast control provided by a 10 kΩ potentiometer. Ag/AgCl electrodes are employed for current injection and voltage sensing across the body. The system is interconnected using jumper wires and implemented on either a breadboard or a custom PCB. Power supply and firmware upload are facilitated through a USB connection. To improve measurement accuracy, a constant current source is designed using operational amplifiers (e.g., OPAMP 07) along with filtering components. Depending on the resolution requirements, analog signals can be digitized using an external ADC. Additionally, protective circuitry, including diodes and transient voltage suppressors, is incorporated to safeguard the hardware from voltage surges, ensuring robust and reliable operation of the device.

Table 1.

Component specification used in developed body segmental BIA device.

S. No.	Component	Description	Specification
1	AD5933	Impedance Converter with frequency generator and DFT engine	Frequency Range: 50 kHz12-bit ADCI2C Interface
2	ESP32-WROOM	Wi-Fi & Bluetooth enabled microcontroller for data processing and control	240 MHz Dual-coreWi-Fi/Bluetooth38 GPIO pinsI2C/UART/SPI support
3	20 × 2 LCD Display	Alphanumeric display for real-time impedance and phase angle values	16 Characters x 4 Lines Parallel Interface 5 V Operation
4	10kΩ Potentiometer	Used to control the contrast of the LCD display	Resistance: 10 kΩ Single-turn Linear taper
5	Connecting Wires	Jumper wires for connections between modules	Current to current, voltage to current, voltage to voltage connections
6	USB Cable	Power supply and programming interface	5 V USB to Micro USB/Type-C (ESP32 dependent)
7	Electrodes (Disposable)	Surface electrodes for current injection and voltage measurement	Ag/AgCl type Gel-based or dry Reusable/disposable
8	Breadboard/PCB	Circuit prototyping platform	Standard solder less breadboard or per fboard
9	ADC	Analog-to-Digital Converter for reading sensor output	Resolution: Depends on required measurement accuracy Interface: SPI, I2C, or other as needed
10	Constant Current source	Amplification and filtering of sensor signals	Operational amplifiers for amplification OPAMP 07 Passive components (resistors, capacitors) for filtering
11	Protection Circuitry	Protect the circuit from overvoltage	Diodes, transient voltage suppressors, etc.

Testing of developed body segmental BIA device

Table 2 presents the testing results of the developed BIA device based on the AD5933 evaluation board. To evaluate system accuracy and reliability, a set of known standard resistors and capacitors, as shown in Fig. 2, were measured and compared by using developed device. The table reports both the standard and measured values of resistance and capacitance, along with the calculated percentage errors. The testing covers a wide range of components, from 10 Ω to 1000 Ω for resistance and 10 pF to 470 pF for capacitance, reflecting typical bio-impedance characteristics encountered in human tissues and bone. The percentage errors remain consistently low, generally within 5%, indicating strong measurement precision and repeatability. As shown in Fig. 3, these results confirm the device’s capability to provide accurate impedance measurements, thereby establishing its suitability for biomedical applications such as bone health monitoring, tissue characterization, and clinical diagnostics.

Table 2.

Resistance and capacitance measurement using developed BIA device.

No.	Standard Resistance (Ω)	Measured Resistance (Ω)	Error (%)	Standard Capacitance (pF)	Measured Capacitance (pF)	Error (%)
1	10	9.50	5.00%	10	9.5	5.00%
2	22	21.40	2.73%	12	12.2	1.67%
3	33	32.80	0.61%	15	14.30	4.67%
4	39	38.30	1.79%	18	17.40	3.33%
5	47	46.02	2.09%	22	21.50	2.27%
6	50	49.50	1.00%	25	24.60	1.60%
7	68	67.60	0.59%	33	34.20	3.64%
8	82	81.20	0.98%	47	46.70	0.64%
9	100	99.20	0.80%	68	67.80	0.29%
10	120	119.50	0.42%	82	81.20	0.98%
11	150	153.00	2.00%	100	101.5	1.50%
12	200	208.00	4.00%	120	119.50	0.42%
13	220	223.50	1.59%	150	151.70	1.13%
14	330	329.50	0.15%	180	182.30	1.28%
15	390	389.30	0.18%	220	218.50	0.68%
16	470	469.00	0.21%	250	253.70	1.48%
17	510	509.30	0.14%	270	269.80	0.07%
18	560	559.20	0.14%	330	331.50	0.45%
19	680	679.70	0.04%	390	386.40	0.92%
20	1000	980.40	1.96%	470	471.20	0.26%

Fig. 2.

Resistance and capacitance used in validation.

Fig. 3.

Testing result for resistance and capacitance.

Body parameters

Different Body Parameters refer to measurable characteristics of the human body that provide insights into health, composition, and physiological status²¹. These parameters are commonly used in clinical, sports, and research settings to assess physical condition, track changes over time, or evaluate the impact of interventions²². Figure 4 illustrates the block diagram of the BIA system, which is used to estimate various body composition parameters based on the electrical properties of biological tissues²³. In this system, a small alternating current from a current source is passed through the human body, and the resulting voltage is measured to determine two key parameters - Resistance (R) and Reactance (X)²⁴. The human body can be modeled as consisting of fat-free mass (body cell mass and extracellular mass) and fat mass, each contributing differently to the impedance characteristics^25–27. The BIA analyzer thus serves as a non-invasive and efficient method for body composition assessment through electrical impedance measurements¹⁹.

Fig. 4.

The bio-impedance analysis block diagram²⁸.

In bio-impedance analysis, the human body is modeled as an electrical circuit consisting of resistive and capacitive components^29,30. The total body impedance (Z) represents the opposition offered by body tissues to the flow of an applied alternating current (AC). Mathematically, it is expressed as^31–33:

1

where R denotes the resistance, primarily associated with the body’s extracellular and intracellular fluids, and represents the capacitive reactance, which arises due to the cell membranes acting as capacitors.The magnitude of the impedance is given by:

2

which reflects the combined effect of resistance and reactance on current flow through body tissues. The phase angle (φ), defined as

3

indicates the phase shift between voltage and current. A higher phase angle suggests better cellular integrity and higher body cell mass, while a lower phase angle may indicate cell membrane damage or poor nutritional status^34,35.

Body mass index (BMI):

BMI is a simple and widely used measure to assess whether a person has a healthy body weight for their height. It is calculated using the formula³⁶:

4

BMI helps classify individuals into categories such as underweight, normal weight, overweight, and obese³⁷. Although it does not directly measure body fat, it serves as a useful screening tool for identifying weight-related health risks and assessing general nutritional status in both clinical and research settings³⁸.

Body Mass:

Body mass refers to the total weight of an individual, which includes all the components that make up the human body such as fat mass, fat-free mass, bone, water, and organs³⁹. In the context of BIA, total body mass is divided into fat mass (FM) and fat-free mass (FFM)⁴⁰.

5

Fat mass represents the total lipid content stored in adipose tissues, while fat-free mass consists of body cell mass (BCM), extracellular mass (ECM), and body fluids^41,42. The fat-free mass is primarily responsible for the body’s metabolic activity and electrical conductivity, as it contains most of the body’s water and electrolytes. The FFM is given by⁴³:

6

Through BIA, body mass distribution can be analyzed non-invasively, providing valuable insights into an individual’s body composition and overall health^44,45.

Methods and subjects

The study was conducted in accordance with the Declaration of Helsinki and received approval from the RAC Institutional Committee of Gautam Buddha University on April 29, 2021 (Ref. No.: GBU-001/SoE/EE/4.2/2021-004). Informed consent was obtained from all participants prior to their inclusion in the study. The research was carried out in collaboration with Yatharth Hospital, Greater Noida, Uttar Pradesh, during the period from August 2024 to March 2025. Institutional approval was also obtained from Yatharth Hospital, and all ethical procedures were strictly adhered to throughout the study. Written informed consent was signed by each participant before enrollment.

An initial pool of 125 individuals with various types of bone fractures was screened for eligibility. During enrollment, demographic and clinical information such as name, age, type of fracture, weight, and address was recorded. Segmental body impedance phase angle, resistance, and reactance values were measured using the developed BIA device under the direct supervision of attending physicians. All measurements followed standardized protocols to ensure accuracy, reliability, and consistency.

From the initial cohort, 125 patients with confirmed fractures were selected for continued monitoring. However, 60 participants declined follow-up participation, leaving 65 individuals who agreed to follow-up. As shown in Fig. 5, the overall study workflow included initial assessment, BIA measurements, and follow-up data collection. For the follow-up group, additional segmental impedance and phase angle measurements were obtained after fracture healing, using the same BIA device. All assessments were conducted under standardized conditions to minimize variability and maximize data reliability.

Fig. 5.

Overview of patient data collection.

Result & discussion

This section describes the comparative analysis of body impedance parameters obtained from fractured and non-fractured individuals. It presents the variations in resistance, reactance, impedance magnitude, and phase angle, highlighting their relationship with physiological changes during bone fracture and healing. The section also discusses the effectiveness of the developed BIA device in accurately capturing these parameters and interpreting their significance in assessing tissue recovery.

Patient general data collection

The participant group in this study comprised 65 individuals, with a nearly equal representation of both sexes: 33 males and 32 females as shown in Fig. 6. This balanced sex distribution provides a robust framework for analyzing variations in body composition and bioelectrical impedance parameters between fractured and healthy states, ensuring that the findings are not biased toward either gender.

Fig. 6.

Patient distribution.

The bar chart in Fig. 7 illustrates the relative frequency (%) of males and females across different age groups, ranging from 22 to 42 years. The male age distribution is highly concentrated within a narrow age band, showing that the majority of individuals fall between 28 and 36 years. The female age distribution indicates that the majority of the female population falls within the 25–30 years age range Fig. 8.

Fig. 7.

Patient age distribution (a) Male (b) Female.

Fig. 8.

Patient weight distribution (a) Male (b) Female.

The weight distributions of males and females show distinct patterns. Male weights range widely from 68 to 86 kg, with most individuals concentrated between 72 and 82 kg, and the highest frequency at 74 kg. In contrast, female weights fall within a narrower range of 52–61 kg, with a strong clustering around 54–57 kg and a peak at 56 kg. Overall, males exhibit a broader and more varied distribution, while females show a more compact and uniform pattern.

The BMI is calculated using⁴⁶. The t-test performed for BMI as shown in Table 3 compares the mean BMI values of males and females. The results indicate whether there is a statistically significant difference between the two groups.

Table 3.

Independent samples t-test for BMI data.

Data Distribution		Sample 1(Male)	Sample 2 (Female)
Sample size		33	32
Arithmetic mean		25.0758	21.8844
95% CI for the mean		24.7561 to 25.3954	21.6700 to 22.0988
Variance		0.8125	0.3536
Standard deviation		0.9014	0.5947
Standard error of the mean		0.1569	0.1051
T-test (assuming equal variances)
Difference		−3.1914
Pooled Standard Deviation		0.7660
Standard Error		0.1900
95% CI of difference		−3.5711 to −2.8116
Test statistic t		−16.794
Degrees of Freedom (DF)		63
Two-tailed probability		P < 0.0001
Residuals
D’Agostino-Pearson test for Normal distribution		accept Normality (P = 0.0627)

A similar independent t-test is conducted for FFM results are presented in Table 4. The FFM data has been calculated using⁴⁶. Since FFM is strongly influenced by muscle mass, males generally show higher FFM values than females.

Table 4.

Independent samples t-test for FFM data.

Data Distribution	Sample 1 (Male)		Sample 2 (Female)
Sample size	33		32
Arithmetic mean	57.7606		36.8312
95% CI for the mean	56.9188 to 58.6024		36.4410 to 37.2215
Variance	5.6356		1.1719
Standard deviation	2.3739		1.0825
Standard error of the mean	0.4132		0.1914
T-test (assuming equal variances)
Difference	−20.9294
Pooled Standard Deviation	1.8545
Standard Error	0.4601
95% CI of difference	−21.8488 to −20.0099
Test statistic t	−45.489
Degrees of Freedom (DF)	63
Two-tailed probability	P < 0.0001
Residuals
Shapiro-Wilk test for Normal distribution	W = 0.9693 accept Normality (P = 0.1055)

Patient data collection using developed BIA device

In this study, bio-impedance measurements were collected from 65 patients diagnosed with bone fractures. Each participant was assessed twice first during the fractured condition (F) and later after complete bone healing (H) of the same limb segment. Measurements were obtained using a developed BIA device under supervision to ensure accuracy and repeatability. The electrode placement, measurement frequency, and subject posture were kept consistent across both sessions. In addition to bioelectrical parameters, anthropometric information such as age, height, and weight was recorded to support normalization and correlation analysis.

Figure 9 presents the distribution of impedance values collected during the fracture phase and after complete healing. The plot clearly shows a substantial rise in impedance following healing, indicating marked changes in tissue electrical properties. During fracture, lower impedance reflects increased extracellular fluid and disrupted structural integrity, whereas higher post-healing values correspond to restored tissue density and reduced inflammatory effects. This visual trend supports the statistical findings, which demonstrate a highly significant increase in impedance after healing.

Fig. 9.

Impedance data collection distribution (a) During Fracture (b) After Healing.

The Paired samples t-test for Impedance data collection results presented in Table 5 indicated non-normality of the differences, the very large t-value supports a strong and reliable effect. The impedance comparison Fig. 10 displays the change in electrical impedance measured during fracture and after bone healing. The visual distribution shows a substantial upward shift in impedance values following healing, with post-healing readings consistently higher across all samples. This trend is confirmed statistically, where the mean impedance increased from 312.03 Ω during fracture to 578.15 Ω after healing, resulting in a mean paired difference of 266.12 Ω. The paired t-test revealed this increase to be highly significant (t = 97.70, df = 64, p < 0.0001), indicating a pronounced electrical property change in the tissue during the healing process.

Table 5.

Paired samples t-test for impedance data collection.

Data Distribution	Sample 1 (Z During Fracture)	Sample 2 (Z After Healing)
Sample size	65	65
Arithmetic mean	312.0308	578.1538
95% CI for the mean	307.5419 to 316.5197	568.6890 to 587.6187
Variance	328.1865	1459.0385
Standard deviation	18.1159	38.1974
Standard error of the mean	2.2470	4.7378
Paired samples t-test
Mean difference	266.1231
Standard deviation of differences	21.9606
Standard error of mean difference	2.7239
95% CI of difference	260.6815 to 271.5646
Test statistic t	97.700
Degrees of Freedom (DF)	64
Two-tailed probability	P < 0.0001

Fig. 10.

Impedance difference analysis.

Figure 11 illustrates the distribution of phase-angle measurements recorded during fracture and after healing. The post-healing phase angle shows a consistent upward shift compared to fracture-phase values, indicating improved cell membrane integrity and enhanced capacitive behavior of regenerating tissues. The visual separation between the two distributions aligns with the t-test results, confirming a significant rise in phase angle after healing. This trend reinforces the sensitivity of phase-angle measurement as a useful parameter for assessing physiological recovery during bone healing.

Fig. 11.

Phase angle data collection distribution (a) During Fracture (b) After Healing.

Table 6 summarizes the paired t-test results comparing phase-angle measurements taken during the fracture condition and after complete bone healing. The mean phase angle increased from 4.96° during fracture to 6.94° after healing, resulting in a mean paired difference of 1.98°. This rise indicates a measurable improvement in tissue electrical properties as healing progresses. The paired t-test confirms that this increase is highly significant (t = 53.06, df = 64, p < 0.0001), demonstrating that phase angle is strongly affected by structural and compositional changes that occur during bone repair. Figure 12 visually reinforces these findings by showing a clear upward shift in phase-angle values after healing. The distribution of post-healing phase-angle measurements consistently lies above the fracture-phase values for almost all individuals. This graphical separation supports the statistical results and highlights the sensitivity of phase angle to biological recovery.

Table 6.

Paired samples t-test for phase angle data collection.

Data Distribution	During Fracture	After Healing
Sample size	65	65
Arithmetic mean	4.9600	6.9369
95% CI for the mean	4.8825 to 5.0375	6.7959 to 7.0780
Variance	0.09775	0.3239
Standard deviation	0.3126	0.5691
Standard error of the mean	0.03878	0.07059
Paired samples t-test
Mean difference	1.9769
Standard deviation of differences	0.3004
Standard error of mean difference	0.03726
95% CI of difference	1.9025 to 2.0514
Test statistic t	53.057
Degrees of Freedom (DF)	64
Two-tailed probability	P < 0.0001

Fig. 12.

Phase angle difference analysis.

Discussion

The overall findings of this study demonstrate clear physiological differences in body composition between males and females, along with pronounced electrical property changes between the fractured and healed bone states. Although males showed higher BMI and FFM values than females, the independent t-tests indicated that these differences were not statistically significant, likely due to high variability within the male measurements. Nevertheless, the trends are consistent with expected biological patterns, where males generally possess greater muscle mass and higher fat-free mass. In contrast, the bio-impedance parameters exhibited highly significant changes between fracture and post-healing conditions. Impedance values increased sharply after healing. Similarly, the phase angle rose markedly in the healed state, indicating improved cell membrane integrity and enhanced capacitive behavior of regenerating tissues. Similar report was published in^47,48, however, they have measured the impedance of rabbit. These shifts in both impedance and phase angle highlight the sensitivity of bioelectrical markers in detecting structural and compositional changes during bone healing. The main findings of this study has been summarized in Table 7.

Table 7.

Summary of key findings.

Parameter	Comparison Groups	Observed Trend	Statistical Outcome	Physiological Interpretation	Clinical Implication
BMI	Male vs. Female	Higher in males	Difference marginally non-significant	Reflects natural variation in body habitus and musculature between sexes	Useful for general profiling, but not a strong discriminator for fracture-related physiological change
Fat-Free Mass (FFM)	Male vs. Female	Males > Females	Difference non-significant due to high variability	Males typically have higher muscle mass and total lean tissue	Not directly influenced by the fracture-healing process; serves as baseline compositional data
Impedance (Z)	During Fracture vs. After Healing	Significantly higher after healing	Highly significant (p < 0.0001)	Healing reduces edema, restores structural integrity, and normalizes tissue conductivity	Strong indicator for tracking healing progress and tissue recovery
Phase Angle (φ)	During Fracture vs. After Healing	Marked increase after healing	Highly significant (p < 0.0001)	Increased phase angle reflects improved cell membrane health, higher capacitive response, and normalized cellular function	Reliable non-invasive marker for monitoring bone and soft tissue repair
Tissue Condition (Overall)	Fractured vs. Healed	Healing associated with improved electrical properties	Strong statistical evidence	Indicates reduction in inflammation, fluid redistribution, and restoration of tissue architecture	Supports the role of BIA as an objective fracture-monitoring tool

Conclusion and future scope

This study demonstrated that BIA can serve as a reliable and non-invasive approach for assessing physiological changes associated with bone fractures and subsequent healing. By evaluating 65 patients during both the fracture phase and after complete healing, clear and statistically significant alterations were observed in key electrical parameters, particularly impedance and phase angle. Impedance values increased substantially after healing, reflecting reduced inflammation, normalized fluid distribution, and restoration of tissue structure. Similarly, the marked rise in phase angle signified improved cell membrane integrity and enhanced capacitive properties of the recovering tissues. These findings collectively confirm that BIA is highly sensitive to the structural and compositional transitions that occur during bone repair. The developed single-frequency segmental BIA device proved effective in capturing these changes with high accuracy, as validated through standardized resistor, capacitor measurements. Its portable design, low cost, and reproducibility make it a practical solution for clinical environments where continuous or repeated monitoring is required. The developed bio-impedance device successfully captured repeat measurements under controlled conditions and showed stable performance during bench top validation using known resistive and capacitive standards. Nevertheless, the system has not been validated against established commercial bio-impedance analyzers, nor has inter-subject reproducibility or in-vivo measurement stability been formally assessed. As such, the present work should be regarded as a technology demonstration and feasibility study, rather than a clinically validated diagnostic or monitoring tool.

Future scope

Future studies should include systematic stratification of participants based on fracture site, type, and severity to allow more precise evaluation of bio-impedance responses across different injury categories. Collecting detailed clinical information including immobilization duration, rehabilitation phase, and physical activity levels will help establish clearer relationships between healing stages and bio-impedance trends. Longitudinal studies are recommended to evaluate temporal variations in bio-impedance parameters from the acute phase through complete recovery. Additionally, incorporating a larger and more diverse sample size, along with advanced analytical models, may improve the reliability and predictive ability of bio-impedance based monitoring. These enhancements will support the development of standardized protocols for the use of bio-impedance as a non-invasive tool for fracture assessment and monitoring in clinical settings.

Author contributions

Km Brajesh : Data Collection, Writing – original draft, Mahmood Aldobali: Methodology, Conceptualization, Kirti Pal : Supervision, Writing – review & editing, Munna Khan: Supervision, Validation.

Funding

The financial assistance is provided by Council of Scientific and Industrial Research (CSIR) CSIR [09/10139(13623)/2022-EMR-1] Govt. of India for carrying out this work.

Data availability

The datasets used and/or analyzed during the current study available within the manuscript.

Declarations

Competing interests

The authors declare no competing interests.

Institutional review board statement

The study was conducted in accordance with the Declaration of Helsinki and approved by RAC Institutional Committee of Gautam Buddha University on April 29, 2021. (Ref. No.: GBU-001/SoE/EE/4.2/2021-004). Informed consent was obtained from all subjects involved in the study.