The present case-control study was conducted in the Department of Physiotherapy, School of Healthcare and Allied Sciences (SoHAS), G D Goenka University, Gurugram, Haryana, India from November 2023 to April 2024. After obtaining ethical clearance from the Institutional Ethics Committee (IEC) (IEC/MPTNEURO/35-36), subjects who met the inclusion and exclusion criteria were selected.

Sample size calculation: The sample size of 66 for the present study was calculated using G-Power 3.1.9.4 software, with a significance level of 5%, a power (1-beta) of 80% and an effect size of 0.4 across three groups. To account for a 20% dropout rate, the final sample size was adjusted to 80.

Inclusion criteria: Individuals aged 18-30 years, comprising both males and females across all groups were included in the study. For the normal weight group (control), participants had a BMI ranging from 18.5-22.9 kg/m². The overweight group (case) included individuals with a BMI of 22.9-24.9 kg/m², while the obese group (case) consisted of participants with a BMI exceeding 25 kg/m².

Exclusion criteria (for cases and controls): Participants in both the control and overweight/obese groups (cases) were excluded if they had any of the following: recent trauma (e.g., fractures, injuries to the upper or lower extremities, traumatic brain injury, or traumatic spinal cord injury), a history of neurological disorders (such as epilepsy, multiple sclerosis, etc.), psychological disorders (such as major depression, anxiety, etc.), congenital disorders (e.g., kyphosis, scoliosis, Marfan syndrome, etc.), recent surgical history, cardiovascular diseases (e.g., hypertension, coronary artery disease, etc.), systemic diseases (e.g., diabetes mellitus, chronic kidney disease, etc.), severe low back pain, disorders affecting special senses (e.g., vision or hearing impairments, etc.), or congenital/acquired lower limb deformities (e.g., flat feet or other lower limb deformities).

The informed consent was obtained from all participants, who were thoroughly briefed about the procedure. The participants were categorised into three groups, ensuring that age and gender were matched, according to the Asian Pacific BMI classifications: 29 participants with normal weight (BMI 18.5-22.9 kg/m²), 26 participants with overweight (BMI 23-24.9 kg/m²), and 35 participants with obesity (BMI >25 kg/m²). BMI was measured as BMI=weight (in kilograms)/height (in meters squared) [12].

Body composition, the Single Leg Standing (SLS) test, the Timed Up and Go (TUG) test, sidewalking, tandem walking and heel walking were used as outcome measures. A BCA machine was used for body composition measurement, and a floor marker was utilised for measuring the distance during the balance and coordination tests. The specified outcome measures were evaluated for all participants, and subsequently, data were collected, gathered, and analysed both manually and digitally to evaluate the effect of BMI on coordination, static balance, and dynamic balance in young adults.

The BCA machine (Tanita Corp., Tokyo, Japan), abbreviated as BCA, analysed body composition using the bioelectrical impedance analysis method to measure the bioelectrical impedance of various parts of the human body. It calculated the body composition through algorithms based on the difference in conductivity of different components of the human body. The BC-418 8-contact electrode system (Tanita Corp., Tokyo, Japan) took all measurements at 50 kHz and 0.8 mA sine wave constant current. A total of five segments were measured: each arm, each leg and the remainder (trunk and head), with the whole body measured as the foot-hand electrical pathway. Lean soft tissue and percentage of fat for each region were provided, and the percentage of fat of the whole body was based on foot-hand impedance measurement [13].

The SLS test was used to assess and evaluate static balance. In this test, the subject was instructed to stand on one leg for 30 seconds with a hand by the side of the trunk. This test was repeated the same way for the other leg, both with eyes open and closed. Three measurements for each side and each state were taken in seconds, and the best of the three was considered as the result. The TUG test was used to assess and evaluate dynamic balance. In this test, the subject was asked to get up from a standard chair, walk straight for 3 meters, return, and then sit back down in the same chair, all at maximum speed without running. Three measurements of this entire process were taken in seconds, and the best of the three was considered as the test result [14].

A total of three tests were used to evaluate coordination skills: sideways walking, tandem walking and heel walking. Each test was conducted over a distance of 10 meters, with the starting position of both hands placed on the Anterior Superior Iliac Spine (ASIS). The command given was ‘Get Set Go,’ and the walking was done at a comfortable speed. The total time was recorded, and three readings were taken, with the mean value of the three considered as the result.

For sideways walking, the subject was instructed to walk sideways by placing the governing leg into abduction and the subordinate leg into adduction. The medial line of both feet was to touch and remain in contact with each other. Both the step count and the total time taken were recorded.

For tandem walking, the subject was instructed to make contact between the toe of one foot and the heel of the preceding foot, walking straight in this manner. The same process was repeated with the rear foot placed in front of the front foot.

For heel walking, the subject was asked to walk on their heels while lifting the forefoot off the ground and pointing the toes outward. Initially, the subject was instructed to place the left foot ahead of the right foot, and the same repetition was done for the right foot [15].

Data analysis was conducted using the Statistical Package for the Social Sciences (SPSS) software version 25.0 (International Business Machines (IBM), US. Descriptive statistics were employed to analyse and determine the mean and standard deviation of the demographic and anthropometric profiles, as well as, the outcome measures of the subjects. Each measure was normally distributed, as assessed by the Shapiro-Wilk test. The p-value and F-values were generated for group differences using the one-way ANOVA method. Post-hoc tests were utilised to evaluate the differences among specific groups, with significance set at p-value <0.016.

There were 29 subjects in normal weight group, 26 subjects in overweight group and 35 subjects in obese group. The demographic profile of the study participants, as shown in [Table/Fig-1], reveals that the mean ages of the normal weight, overweight and obese groups were 22.10±2.38 years, 21.77±2.90 years and 21.91±2.38 years, respectively. The mean heights of the normal weight, overweight and obese groups were 162.63±10.71 cm, 163.04±11.57 cm and 156.26±27.11 cm, respectively. The mean weights of the normal weight, overweight and obese groups were 53.90±8.33 kg, 64.25±9.14 kg and 77.16±13.01 kg, respectively. The mean BMI of the normal weight, overweight and obese groups were 20.23±1.30 kg/m², 23.99±0.68 kg/m² and 29.69±3.09 kg/m², respectively, as shown in [Table/Fig-1]. The data comprised nearly equal numbers of males and females across all three groups: the normal weight group included 16 males and 13 females, the overweight group had 14 males and 12 females, and the obese group consisted of 19 males and 16 females. The body composition of the study participants indicated that the mean fat mass of the normal, overweight and obese groups was 24.26±6.19%, 27.82±6.60%, and 32.83±6.96%, respectively, while the mean Fat-free Mass (FFM) of the normal, overweight and obese groups was 75.74±6.19%, 72.18±6.60% and 67.17±6.96%, respectively.

The static and dynamic balance profiles of the study participants are illustrated in [Table/Fig-2]. The mean SLS test timing with open eyes on the left and right legs for the normal, overweight and obese groups were 28.86±3.85, 25.96±7.97 and 24.47±7.94 seconds, respectively. The mean SLS test timing with closed eyes on the right leg were 15.79±6.33, 10.65±4.24 and 6.69±4.65 seconds, respectively; the mean SLS test timing with closed eyes on the left leg were 17.93±6.19, 11.38±4.14 and 6.74±3.71 seconds, respectively. Lastly, the mean Timed Up and Go (TUG) test timing for the normal, overweight and obese groups were 9.07±1.39, 11.88±3.25 and 16.74±3.65 seconds, respectively.

The coordination profile of the study subjects across all three groups shows that the mean timing for sidewalking for normal weight, overweight and obese individuals were 32.17±4.79 seconds, 39.54±4.81 seconds and 44.86±8.60 seconds, respectively. The mean timing for tandem walking for the normal, overweight and obese groups were 30±6.34 seconds, 39.30±11.07 seconds and 50.31±11.84 seconds, respectively. The mean timing for heel walking for normal weight, overweight and obese groups were 26±4.98 seconds, 32.70±6.18 seconds and 43.71±7.75 seconds, respectively.

The one-way ANOVA test indicated a significant difference in the static and dynamic balance of normal, overweight and obese subjects, with F-value=3.20, p-value=0.043 for the SLS test with open eyes on the left and right legs; F-value=24.77, p-value <0.001 for the SLS test with closed eyes on the right leg; F-value=43.93, p-value <0.001 for the SLS test with closed eyes on the left leg; and F-value=54.66, p-value <0.001 for the TUG test, with F_critical=3.101.

There was a significant difference in coordination among normal weight, overweight and obese subjects, with F-value=29.74; p-value <0.001 for sidewalking, F-value=32.05; p-value <0.001 for tandem walking, and F-value=60.52; p-value <0.001 for heel walking.

In the post-hoc analysis, the Bonferroni method was applied for multiple comparisons to adjust the alpha level, which was determined by dividing the original alpha level by the number of comparisons, resulting in 0.05/3 ≈ 0.016. The analysis of Single Leg Stance (SLS) with open eyes on the right and left legs showed significant differences between the normal and overweight groups, as well as between the obese and normal weight groups, with p-values <0.001. These results are significant given the Bonferroni-adjusted alpha level of 0.016. However, there was no significant difference between the overweight and obese groups [Table/Fig-3].

The post-hoc analysis for SLS with closed eyes on the right leg revealed significant differences between normal and overweight (p-value <0.001), overweight and obese (p-value=0.001), and obese and normal weight (p-value <0.001). For the left leg, significant differences were observed among the normal weight, overweight and obese groups, all with p-value <0.001.

The Timed Up and Go (TUG) post-hoc test showed significant differences between normal and overweight overweight and obese, and obese and normal weight, all with p-value <0.001. Similarly, the sidewalk post-hoc test revealed significant differences between normal and overweight overweight and obese, and obese and normal weight, all at p-value <0.001. The tandem walking and heel walking post-hoc tests also indicated significant differences among the groups, with p-values <0.001 for all comparisons [Table/Fig-3].

The results of the present study clearly show that changes in body composition, specifically increased fat mass, can influence BMI and WHR proportionately and have an inverse association with coordination, as well as, static and dynamic balance, accepting alternative hypothesis. The statistics presented in the study indicate significant differences in coordination, static balance and dynamic balance among different BMI groups when inter group comparisons were made. Increased fat mass, BMI and WHR have detrimental effects, as evidenced by decreased scores in the SLS test with open eyes on both legs, the SLS test on the right leg, and the SLS test on the left leg; as well as, increased scores in the TUG test, sidewalking, tandem walking and heel walking.

A study by Kumar M and Arya P has pointed out through their research that an elevated BMI can be associated with excess fat accumulation around the abdominal area, leading to weakened abdominal muscles and consequently changing the alignment of the body’s Centre Of Gravity (COG) by increasing lumbar lordosis [16]. Further, the study by Mohebi Rad Z and Norasteh AA aligns with the present research and supports the idea that core muscles play a vital role in regulating limb movement, stabilising the trunk and lumbopelvic regions, distributing stress, and supporting body weight during various activities. Increased fat mass, particularly in the abdominal area, diminishes the core muscles’ ability to manage postural fluctuations, leading to a higher risk of falls in obese and overweight individuals. This occurs due to a disproportion between the COG, the Line of Gravity (LOG) and the Base of Support (BOS), caused by impaired core muscle biokinetics. Increased fat mass is inversely related to balance capability due to greater COG displacement [17]. Another study by Kumar M et al., found a correlation between flat feet and obesity in middle-aged individuals, which disrupts the kinematic chain and is associated with structural changes in the feet. This supports the study’s rationale that obesity can lead to structural alterations in the feet, resulting in flat feet and potentially affecting balance through kinematic chain disruption [18].

Body composition also adversely affects coordination, as increased fat mass, BMI and WHR collectively increase the time required to complete tests designed to evaluate coordination in all three groups. A study by D’Hondt V et al., has aligned the same pathology behind poorer coordination with increasing BMI and WHR. The probable reason given is that greater abdominal fat mass diminishes core muscle capacity for dynamic postural control, challenging coordination amidst increased body mass, movement and gravitational forces, which require enhanced strength, endurance and explosiveness. The present study has shown that having a higher fat mass percentage negatively impacts both static and dynamic balance [19].

Another study by Mocanu GD and Murariu G stated that children aged 12-15 years with elevated BMI values, particularly those classified as overweight and obese, exhibited diminished balance capability in the anterior reach direction compared to their peers with normal weight. Variations in muscle strength, particularly in the flexors and extensors of the lower limbs, were evident between obese and normal weight individuals. Greater abdominal fat mass challenges coordination due to reduced core muscle capacity for dynamic postural control. Additionally, the study highlighted the adverse effects of excess weight on postural control and a reduction in upper limb movement efficiency. However, the study recognised limitations, such as the absence of underweight participants in the sample, which is also a limitation of the current study [20].

Results from a study by Türker A and Yüksel O showed statistically significant developmental variances in metrics such as Maximal Oxygen Consumption (VO2) estimate (mg/kg/min), Metabolic Equivalent (MET) fat percentage and the left foot lateral balance test, which were achieved by providing classical and functional strength training. From the present study, it can be inferred that classical and functional strength training has reduced fat mass percentage, which, in turn, significantly improved balance capability, supporting the findings of the current research [21].

The clinical implications of the present study not only highlights the variations in neuromuscular biomechanics, such as coordination and static and dynamic balance, based on body composition, but also emphasise the physiotherapeutic impact in the assessment, evaluation, treatment and prevention of metabolic pathologies such as obesity.

The present study had limitations in that it did not assess the amount and type of physical activity in which participants engaged, which could influence balance and coordination. Additionally, nutritional habits and deficiencies, which can impact physical performance-including balance and coordination were not evaluated through dietary intake assessments.

The findings of the present study suggest that there are significant differences in balance and coordination among young adults based on their BMI categories. Specifically, the study indicates that individuals of normal weight, those who are overweight and those who are obese exhibit clear and statistically significant distinctions in their balance and coordination abilities. Changes in body composition and proportional alterations in BMI inversely impact coordination, as well as, static and dynamic balance. This implies that as BMI increases, there is a corresponding decrease in balance and coordination abilities. Overall, the present study underscores that higher BMI levels adversely affect balance and coordination in young adults. This finding could have important implications for interventions aimed at improving physical health outcomes, particularly in addressing the impact of weight management on motor skills and overall functional abilities.