Effect of age-related hyperkyphosis on open and closed-loop postural control in older adults

Abstract

Center-of-pressure (CoP) oscillations represent the neuromuscular system’s response to control the body’s center of mass. In older adults, hyperkyphosis alters body alignment, increases sway, and impairs proprioception. While some studies have explored hyperkyphosis effects on CoP displacements, the underlying neuromuscular mechanisms remain underexplored. This studyinvestigated hyperkyphosis impacts on the postural control using the stabilogram diffusion analysis (SDA) to assess interplay between open and closed-loop control. Thirty-eight older adults with a mean kyphosis angle of 57.8 ± 8.4° and 34 controls with a mean kyphosis angle of 38.4 ± 4.9°, participated. CoP parameters, including trajectory range, velocity in the anterior–posterior (AP), mediolateral (ML), and planar (R) directions, sway area per unit time, and SDA, were measured during bipedal standing in eyes-open (EO) and eyes-closed (EC) conditions. Results showed significantly higher short-term effective diffusion coefficients in the ML (p = 0.016), AP (p = 0.011), and R-directions (p = 0.007), as well as critical displacement in the AP-direction (p = 0.048), and CoP velocity in R-direction (p = 0.046) in the hyperkyphotic group. Conversely, critical time interval in R-direction (p = 0.034) was lower compared to controls. EC increased short-term effective diffusion coefficient in all directions, critical displacement in the AP, and R-directions, sway area per unit time, CoP velocity in all directions, trajectory range in the AP (p < 0.001) and ML-directions (p = 0.047). EO showed higher long-term diffusion coefficients in the AP and R-directions (p < 0.001), and critical time intervals in the AP (p = 0.014) and R-direction (p = 0.003). Hyperkyphosis impairs open-loop control and reliance on closed-loop mechanisms, potentially delays responses and increases fall risk in older adults.

Introduction

Age-related hyperkyphosis that refers to an exaggerated forward curvature of the thoracic spine with a curvature angle of greater than 50° is a prevalent disorder in individuals over the age of 65, with a multifactorial etiology^1,2. In addition to its aesthetic implications, hyperkyphosis has been linked to a range of functional and quality-of-life concerns, including respiratory capacity, physical performance, and the ability to perform activities of daily living³. The association between hyperkyphosis and fall risk in older adults has been well established⁴. An increased kyphosis angle shifts the body’s center of gravity forward, resulting in an augmented forward bending moment in the trunk⁵. Consequently, the center of pressure (CoP) moves anteriorly; to counteract this increased bending moment and control the CoP, trunk extensor muscle activity increases⁶, ultimately leading to greater body sway⁷. Moreover, research has demonstrated that older adults with hyperkyphosis exhibit reduced trunk muscle strength and endurance, as well as impaired trunk positional sense⁸. These increased body sways, combined with diminished trunk muscle performance and impaired proprioception, place older adults at an elevated risk of falling. In light of the potential for adverse outcomes such as hospitalization, loss of independence, and even mortality, falls in this population are regarded as significant health concerns^9,10.

Hyperkyphosis shifts the CoP forward and impairs postural stability¹¹. To maintain the center of mass within the base of support, older adults must adopt various compensatory strategies. These strategies are associated with changes in sagittal spinal alignment and postural muscle activity^6,12. Although some studies have examined the effects of hyperkyphosis on balance from linear analysis approaches^7,13–15, the underlying neuromuscular mechanisms, particularly differences in the use of postural muscle activity and sensory feedback in maintaining balance, have yet to be fully investigated. Linear analysis approaches focus on the magnitude of sway while ignoring time-dependent changes in the pattern or structure of CoP data, potentially missing subtle alterations in postural control¹⁶. Since CoP oscillations are the response of the neuromuscular system to control the center of mass position¹⁷, analyzing its behavior can shed light on the dynamics of higher-order postural control. In this context, computational techniques, such as stabilogram diffusion analysis (SDA), facilitate the evaluation of the relative contributions of automatic and voluntary control mechanisms during postural balance tasks.

Stabilogram diffusion analysis, introduced by Collins and De Luca¹⁶, examines the mechanisms underlying postural control over time intervals, was employed to quantify the stochastic properties of the CoP trajectory. Following a disturbance in short time intervals (approximately 1 s), the CoP exhibits persistent behavior by moving away from a relative equilibrium point, an indication of open-loop control. Conversely, during long time intervals, the CoP tends to return to a relative equilibrium point, reflecting antipersistent behavior and indicating a closed-loop control mechanism. The transition between short and long-time intervals, i.e., open-loop and closed-loop control, is called the critical point, reflecting the average time interval (critical time interval) and sway displacement (critical displacement) where the closed-loop mechanisms are engaged to correct CoP deviations. Maintaining postural control requires continuous interaction between the open-loop and closed-loop control mechanisms¹⁸. The open-loop mechanism is feedforward control, and postural muscles activity in the open-loop mechanism is the primary factor in balance control. When the muscles of the trunk and lower limbs are not able to generate sufficient activity to balance control, the closed-loop mechanism, which uses sensory feedback (through the somatosensory, visual, and vestibular systems), corrects random changes in muscle tone and joint movement and helps maintain and balance control^16,18.

In general, postural control relies on integrating somatosensory, vestibular, and visual inputs to achieve postural orientation and postural balance¹⁹. While measures such as CoP displacement and velocity are commonly employed, they do not fully capture the dynamic nature of neuromuscular control. In contrast, SDA provides a stochastic modeling framework that differentiates between open-loop control (pre-programmed motor responses), and closed-loop control (sensory feedback correction)¹⁶. It has also proven to be a reliable method for investigating static balance control mechanisms across various applications, including in older adults¹⁸ and individuals with a history of falls²⁰. Collins and De Luca¹⁸ demonstrated that open-loop control, the critical time interval, and displacement were higher in older adults compared to young adults. Additionally, a study investigating falls in older adults revealed that, among individuals who sustained serious injuries and those who fell but remained uninjured, only open-loop control and critical displacements in the AP direction were higher compared to non-faller older adults²⁰. Aging and changes in sagittal spinal alignment can modify postural muscle activity and decrease proprioceptive input^8,21, potentially disrupting the interaction between open-loop and closed-loop balance control mechanisms in older adults with hyperkyphosis. It is well established that the availability of sensory information, such as visual and proprioceptive cues, plays a critical role in stabilizing posture^22,23. Confirming this, previous studies have demonstrated that optical feedback contributes to better stabilization, particularly in short-term stabilogram-diffusion analysis measures²⁴. Building on these principles, this study aimed to investigate the interplay between open-loop and closed-loop control mechanisms to gain insight into the effects of age-related hyperkyphosis on postural control mechanisms. Additionally, we investigated how hyperkyphosis influences the CoP trajectory range and mean velocity in older adults. First, we hypothesized that individuals with hyperkyphosis would exhibit a larger critical displacement and an extended critical time interval, reflecting altered balance control strategies and impaired coordination between open-loop and closed-loop mechanisms. Second, we hypothesized that the interaction between visual conditions (eyes-open vs. eyes-closed) and group (hyperkyphotic older adults vs. age-matched controls) would influence balance control mechanisms. Specifically, we expected that visual feedback would have a stronger stabilizing effect in individuals with hyperkyphosis due to their altered postural alignment and potential proprioceptive deficits. Third, we hypothesized that hyperkyphosis would shift the CoP forward, leading to greater mean CoP displacement and velocity in the AP direction relative to an age-matched control group.

Results

Table 1 shows the demographic and clinical characteristics of the participants. There were no significant differences in age, height, weight, and gender between the two groups.

Table 1.

Participants’ characteristics. SD standard deviation. *Statistically significant at α = 0.05.

Parameters	Hyperkyphosis (Mean ± SD)	Control (Mean ± SD)	P value
Gender	23F/15 M	16F/18 M	0.344
Age (Years)	65 ± 5.4	65.1 ± 5.3	0.944
Height (cm)	160.4 ± 8.5	162.7 ± 8.8	0.349
Weight (kg)	76.6 ± 13.9	71.4 ± 9.9	0.076
Thoracic kyphosis (Degree)	57.8 ± 8.4	38.4 ± 4.9	< 0.001*

Linear analysis of CoP

In the linear CoP parameters, the interaction of group by task difficulty was not significant for any of the postural parameters. For the main effect of task difficulty (eyes closed versus eyes open) sway area per unit time (F(1, 70) = 24.83, p < 0.001), CoPvx (F(1, 70) = 36.80, p < 0.001), CoPvy (F(1, 70) = 141.8, p < 0.001), CoPvr (F(1, 70) = 120.63, p < 0.001), average AP (F(1, 70) = 22.94, p < 0.001), and the ML–CoP range (F(1, 70) = 4.07, p = 0.047) were significantly greater for the eyes-closed condition than for the eyes-open condition. The main effect of group (hyperkyphotic group versus age-matched control group) was significant for CoPvr (F(1, 70) = 4.14, P = 0.046) (Table 2). The mean value of the hyperkyphotic group presented significantly greater CoPvr compared to the age-matched control group (Table 2).

Table 2.

Mixed ANOVA results for investigating the interaction effects of Task*Group and the main effects of Task and Group in the linear analysis of CoP. CoP center of pressure, v velocity, saut sway area per unit time, the subscript x, y, and r show parameters in the mediolateral, anterior–posterior, and planar direction, respectively. P probability of significance for F tests. *Statistically significant at α = 0.05.

Parameters	Condition	Kyphosis Mean ± SD	Control Mean ± SD	Condition P value	Group P value	Interaction P value
CoPx range	Eyes-open	17.94 ± 4.88	16.27 ± 3.8	< 0.001*	0.179	0.877
	Eyes-closed	18.96 ± 6.15	17.17 ± 5.28
CoPy range	Eyes-open	27.23 ± 7.19	25.2 ± 4.4	0.047*	0.124	0.897
	Eyes-closed	30.4 ± 7.87	28.58 ± 6.48
CoPvx	Eyes-open	4.13 ± 1.33	3.58 ± 1.25	< 0.001*	0.102	0.624
	Eyes-closed	4.96 ± 2	4.28 ± 1.92
CoPvy	Eyes-open	6.9 ± 2.42	5.9 ± 1.34	< 0.001*	0.050	0.230
	Eyes-closed	10.41 ± 4.58	8.76 ± 2.72
CoPvr	Eyes-open	8.84 ± 2.82	7.58 ± 1.77	< 0.001*	0.046*	0.288
	Eyes-closed	12.49 ± 5.18	10.59 ± 3.28
Saut	Eyes-open	15.19 ± 8.02	11.54 ± 3.82	< 0.001*	0.068	0.542
	Eyes-closed	21.95 ± 17.89	16.82 ± 8.71

Nonlinear analysis of CoP

In the nonlinear CoP parameters, the interaction of group by task difficulty was not significant for any of the postural parameters. With respect to the main effect of task difficulty Dxs (F(1, 70) = 17, p < 0.001), Dys (F(1, 70) = 63.68, p < 0.001), Drs (F(1, 70) = 20.85, p < 0.001), Hxs (F(1, 70) = 11.42, p < 0.001), Hys (F(1, 70) = 135.4, p < 0.001), Hrs (F(1, 70) = 33.39, p < 0.001), Cdy (F(1, 70) = 19.47, p < 0.001), and Cdr (F(1, 70) = 16.69, p < 0.001) ) were significantly greater for the eyes-closed condition than for the eyes-open condition, and Dyl (F(1, 70) = 60.59, p < 0.001), Drl (F(1, 70) = 13.15, p < 0.001), Hyl (F(1, 70) = 103.37, p < 0.001), Hrl (F(1, 70) = 60.07, p = 0.001), Cty (F(1, 70) = 6.42, p = 0.014), and Ctr (F(1, 70) = 9.32, p = 0.003) were significantly greater for the eyes-open condition than for the eyes-close condition (Table 3). The main effects of group were significant for Dxs (F(1, 70) = 6.09, p = 0.016) Dys (F(1, 70) = 6.89, p = 0.011), Drs (F(1, 70) = 7.76, p = 0.007), Hxs (F(1, 70) = 9.19, p = 0.003), Hys (F(1, 70) = 11.98, p = 0.001), Hrs (F(1, 70) = 12.02, p = 0.001), Cdy (F(1, 70) = 4.05, p = 0.048), and Ctr (F(1, 70) = 4.66, p = 0.034). As shown in Table 3, Dxs, Dys, Drs, Hys, Hxs, Hrs, and Cdy were significantly greater in the hyperkyphotic group, whereas Ctr was significantly lower than that observed in the age-matched control group.

Table 3.

Mixed ANOVA results for investigating the interaction effects of Task*Group and the main effects of Task and Group in nonlinear analysis of CoP. D = effective diffusion coefficient (mm²/s), H = Hurst exponent, Ct = critical time interval (s), Cd = critical displacement (mm²), The subscript s and l show parameters in the short-term and long-term regions, respectively, the subscript x, y and r show parameters in the mediolateral, anterior–posterior and planar direction, respectively. P = probability of significance for F tests. *Statistically significant at α = 0.05.

Parameters	Condition	Kyphosis Mean ± SD	Control Mean ± SD	Condition P value	Group P value	Interaction P value
Dxs	Eyes-open	5.42 ± 3.26	3.67 ± 2.07	< 0.001*	0.016*	0.535
	Eyes-closed	7.01 ± 4.93	4.85 ± 3.58
Dxl	Eyes-open	0.56 ± 0.44	0.46 ± 0.49	0.623	0.560	0.673
	Eyes-closed	0.49 ± 0.65	0.46 ± 0.59
Dys	Eyes-open	11.5 ± 7.21	7.45 ± 2.45	< 0.001*	0.011*	0.143
	Eyes-closed	23.47 ± 18.52	15.67 ± 7.8
Dyl	Eyes-open	1.85 ± 1.56	1.82 ± 0.97	< 0.001*	0.996	0.862
	Eyes-closed	1.13 ± 0.76	1.16 ± 0.97
Drs	Eyes-open	16.92 ± 9.93	11.05 ± 3.84	< 0.001*	0.007*	0.175
	Eyes-closed	30.33 ± 22.28	20.45 ± 9.46
Drl	Eyes-open	2.41 ± 1.81	2.23 ± 1.28	0.001*	0.744	0.664
	Eyes-closed	1.64 ± 1.2	1.64 ± 1.25
Hxs	Eyes-open	0.88 ± 0.05	0.84 ± 0.05	0.001*	0.003*	0.596
	Eyes-closed	0.89 ± 0.06	0.87 ± 0.05
Hxl	Eyes-open	0.16 ± 0.08	0.18 ± 0.12	0.053	0.061	0.581
	Eyes-closed	0.12 ± 0.08	0.17 ± 0.11
Hys	Eyes-open	0.87 ± 0.05	0.82 ± 0.05	< 0.001*	0.001*	0.114
	Eyes-closed	0.92 ± 0.04	0.89 ± 0.05
Hyl	Eyes-open	0.21 ± 0.12	0.26 ± 0.09	< 0.001*	0.265	0.110
	Eyes-closed	0.13 ± 0.13	0.12 ± 0.08
Hrs	Eyes-open	0.87 ± 0.04	0.83 ± 0.04	< 0.001*	0.001*	0.176
	Eyes-closed	0.91 ± 0.04	0.89 ± 0.04
Hrl	Eyes-open	0.2 ± 0.09	0.23 ± 0.09	0.001*	0.088	0.372
	Eyes-closed	0.11 ± 0.06	0.13 ± 0.07
Ctx	Eyes-open	1.41 ± 0.35	1.57 ± 0.41	0.498	0.222	0.181
	Eyes-closed	1.44 ± 0.37	1.46 ± 0.41
Cdx	Eyes-open	12.88 ± 9.37	10.22 ± 7.38	0.114	0.087	0.433
	Eyes-closed	14.63 ± 9.17	10.82 ± 7.66
Cty	Eyes-open	1.26 ± 0.42	1.27 ± 0.42	0.014*	0.206	0.099
	Eyes-closed	1.06 ± 0.22	1.23 ± 0.41
Cdy	Eyes-open	24.04 ± 21.05	15.9 ± 10.22	< 0.001*	0.048*	0.538
	Eyes-closed	32.32 ± 20.36	26.88 ± 12.81
Ctr	Eyes-open	1.21 ± 0.27	1.4 ± 0.47	0.003*	0.034*	0.458
	Eyes-closed	1.11 ± 0.26	1.23 ± 0.39
Cdr	Eyes-open	34.49 ± 25.66	26.26 ± 14.22	< 0.001*	0.064	0.918
	Eyes-closed	45.33 ± 26.7	36.56 ± 17.98

Discussion

The present study employed SDA to examine the interplay between open-loop and closed-loop control in hyperkyphotic older adults and an age-matched control group. The findings indicate that hyperkyphosis influences balance control, particularly through its effects on critical time intervals in the R-direction and critical displacement in the AP-direction, which reflect altered interplay between open-loop and closed-loop mechanisms. However, the interaction between group and visual condition was not statistically significant. Additionally, the study reveals that occluding vision affects both linear and non-linear measures of postural control, contributing to postural instability. Furthermore, the R-direction CoP mean velocity was found to be increased in hyperkyphotic older adults, suggesting altered postural regulation compared to the control group.

The short-term scaling exponents in the ML, AP and R directions were greater in hyperkyphotic older adults compared to age-matched controls. This finding suggests that their postural control system might be less efficient in maintaining stability¹⁸.The short-term effective diffusion coefficients (Ds) in the AP, ML and R directions are greater in hyperkyphotic older adults than in healthy older adults, indicating increased stochastic activity of the CoP in these individuals. Changes in the timing of postural muscles in the trunk and lower limbs⁶, as well as trunk stiffening^25,26 in hyperkyphotic older adults, combined with weakened trunk and hip musculature^8,15,27, may contribute to elevated diffusion coefficients (Ds) and increased body sway²⁸. The compensatory co-contraction of trunk extensors and hip adductors likely act as an adaptive response to declining force production capacity. Furthermore, sensory system disruptions (particularly impaired proprioception) can alter postural control strategies, affecting open-loop mechanisms independently of closed-loop feedback systems²⁹.

Despite the increased rate of long-term sway change observed in hyperkyphotic older adults, no significant difference in Dl between the hyperkyphotic and control groups was found. This contrasts with expectations, as proprioceptive deficits in hyperkyphosis older adults⁸ and the known influence of open-loop on closed-loop control¹⁸ suggested higher Dl values (indicating less effective long-term correction)³⁰. One explanation is that both groups may have adopted similar balance strategies (primarily relying on ankle strategies for postural control) which could homogenize closed-loop control behaviors, as measured by Dl, even though hyperkyphotic individuals often employ hip strategies³¹. Moreover, low task complexity and preserved vestibular inputs or other compensatory mechanisms during eyes closed (EC) trials might reduce the impact of proprioceptive deficits on Dl³². Additionally, the EC condition could have induced a fear of falling in both groups, leading to more cautious and similar postural control strategies³³. Future research should consider more demanding conditions, such as tandem stance or standing on foam to better differentiate the postural control mechanisms between hyperkyphotic older adults and controls.

The results of task difficulty revealed that vision reduced the stochastic activity of open-loop mechanisms and decreased the negatively correlated (corrective) activity of closed-loop control. Riley et al.³⁴ examined the effects of vision occlusion in a forward-bent posture, analogous to hyperkyphosis, where the CoP shifts forward and challenging stability. Similar to our finding, they reported increased short-term intervals, likely referring to increased Ds and Hs, due to the loss of visual feedback.

Also, a decrease in long-term intervals (Hl and Dl) was reported, suggesting that the closed-loop control becomes more active or effective in correcting random fluctuations. This result was in contrast with those recorded in upright standing, where vision occlusion increased both short-term and long-term sway³⁰. The forward-bent posture may already stress the balance system, potentially altering how sensory inputs are weighted³². Loss of vision may prompt the system to mainly rely on somatosensory and vestibular feedback, which could be more effective in this specific posture³⁵. This adaptation might lead to more frequent corrective actions, reducing Hl or Dl. The decrease in long-term intervals under vision occlusion suggests that the closed-loop control system adapts by enhancing corrective actions. This adaptation is specific to the forward-bent posture and does not contradict typical findings but rather highlights a context-dependent response. Supporting evidence from brain connectome studies in unstable stances found that visual input rebalances feedforward and feedback processes, with eyes-closed conditions showing smaller long-term exponents, supporting the idea of increased closed-loop control³⁶.

This study revealed differences in critical displacement in the AP direction and critical time interval in the R direction between hyperkyphotic older adults and age-matched controls. These findings suggest that different postural control strategies are employed by the two groups. The coordinates of the critical point (crossover phenomenon¹⁸) indicates that variables are limited within specific bounds, indicating direct or indirect control behavior over the variables. Body sway is corrected by the postural control system only after surpassing a threshold, with closed-loop control intervening when stability is compromised. Riley et al.^24,34 describe postural sway as a perception–action strategy, where open-loop control serves an exploratory function, gathering information via proprioception and body orientation, while closed-loop control is performatory, using this input to stabilize posture. In hyperkyphotic older adults, impaired trunk proprioception due to muscle fatigue and reduced spindle sensitivity may prompt alternative sensory strategies⁸. Increased CoP fluctuations suggest heightened exploratory behavior in postural control, reflected in greater variability in linear sway parameters.

This study revealed no statistically significant difference in the displacement of the CoP (linear analysis) in the AP or ML directions between hyperkyphotic older adults and age-matched controls. Although a systematic review demonstrates the effects of the hyperkyphosis on falling in older adults⁴, the results of postural control studies remain conflicting. Some studies have indicated that CoP displacement in the AP direction is not related to hyperkyphosis^13,14,37, whereas others have shown a positive relationship between hyperkyphosis and CoP displacement⁷ or a negative relationship³⁸. One possible explanation is the confounding effect of osteoporosis. Osteoporosis, often accompanied by vertebral fractures, altered spinal alignment, reduced muscle strength, and impaired proprioception, can independently affect balance control. In studies reporting an impact of hyperkyphosis on postural balance, participants were frequently diagnosed with osteoporosis^7,38, suggesting that the observed balance impairments might be partly attributable to osteoporotic changes rather than hyperkyphosis alone. Indeed, both osteoporosis and fractures have been shown to impact balance independently³⁹. Unlike previous studies, this study excluded individuals with a T-score greater than −1, thereby isolating the effect of hyperkyphosis on balance. Another possible explanation is the presence of compensatory mechanisms in the lumbar region. Tsai et al.⁴⁰ demonstrated that the extent of spinal deformity distinctly affects inclination (deviation of the spine from vertical alignment) and postural control. Their findings differentiated between “whole kyphosis” and “lower kyphosis”. In whole kyphosis, the curve extends across both the thoracic and lumbar regions, leading to reduced lumbar lordosis and greater anterior displacement of the center of mass. In contrast, lower kyphosis is confined to the thoracic region, where lumbar lordosis remains intact, keeping the center of mass more centrally aligned. Consequently, older adults with whole kyphosis exhibited greater CoP displacement compared to those with lower kyphosis⁴⁰. Similarly, Ishikawa et al.¹⁴ found that lumbar kyphosis had a more pronounced impact on spinal inclination and postural balance than thoracic kyphosis in osteoporotic patients. Unlike thoracic kyphosis, which can be compensated for through adjustments in the lumbar spine, lumbar kyphosis directly affects balance and increases fall risk¹⁴. Although this study did not assess lumbar lordosis, its findings suggest that disruptions in lumbar curvature could contribute to postural instability.

The CoP mean velocity in the R direction was greater in hyperkyphotic older adults than in age-matched controls. The mean velocity in the R direction is calculated as the sum of the mean squares of the displacements in the AP and ML directions. Our findings contradict those of previous studies. Mohebi et al.¹⁵ compared four groups, kyphotic non-osteoporotic, kyphotic osteoporotic, osteoporotic with normal kyphosis, and non-osteoporotic with normal kyphosis, and reported no significant differences in CoP mean velocity between the kyphotic non-osteoporotic group and the non-osteoporotic group with normal kyphosis. This discrepancy may be attributed to differences in the measurement methods (their use of a flexicurve ruler, which exhibits moderate validity, but its accuracy declines with increasing curvature severity⁴¹) and the relatively younger age of their participants (average age under 60 years), given that postural control deteriorates with age³³. Similarly, Greig et al.¹³ reported no velocity difference among older osteoporotic women stratified by kyphosis severity possibly because the average kyphosis angle in the high kyphosis group was below 50°. Two factors may contribute to the increased CoP mean velocity in hyperkyphotic older adults. The first one is impaired proprioception. Jeka et al.⁴² demonstrated that the nervous system relies heavily on both vision and proprioception to receive crucial velocity information about center-of-mass dynamics. When these sensory inputs are degraded, CoP mean velocity increases. Second, fear of falling may drive hyperkyphotic older adults to adopt a stiffening strategy. Benjuya et al.⁴³ showed that, under challenging conditions such as narrow-base and eyes-closed trials, older adults tend to increase muscle co-contraction or “freeze” their lower limbs to compensate for age-related sensory declines. While this strategy enhances stability, it also leads to increased CoP mean velocity^43,44. Although proprioception and fear of falling were not evaluated in this study, overall, these findings suggest that both impaired sensory input and compensatory motor strategies, may underlie the heightened CoP mean velocity observed in hyperkyphotic older adults.

In conclusion, both linear and nonlinear approaches have demonstrated that hyperkyphosis affects postural control in older adults. Although an increase in CoP mean velocity (linear analysis) reflects the displacement of the CoP over time, this metric alone does not confirm impaired postural control and its effect also depends to some extent on the CoP direction in relation to the margin of the base of support⁴⁵. In our study, SDA provided further insight: hyperkyphosis was associated with an increase in critical displacement and short-term effective diffusion coefficients, indicating elevated stochastic activity in the open-loop postural control. This suggests that individuals with hyperkyphosis engage in greater exploratory behavior to maintain balance, resulting in increased variability in CoP displacement and a higher likelihood of instability before corrective closed-loop feedback is applied. Additionally, the reduction in critical time intervals, an indicator of impaired open-loop control, prompting earlier activation of sensory feedback and a greater dependence on closed-loop mechanisms. This reliance may delay compensatory responses needed to counteract sudden perturbations, potentially increasing the risk of postural instability and falls in hyperkyphotic older adults.

Method

Participants

Seventy-two older adults participated in two groups, including 38 older adults with a mean hyperkyphosis of 57.8 ± 8.4° degrees and 34 older adults with a mean hyperkyphosis of 38.4 ± 4.9°. Participants were recruited through public announcements and advertisements. Individuals over 60 years of age with a thoracic kyphosis angle (TKA) of more than 50° for the hyperkyphotic group and less than 50° for the age-matched control group were included if they could stand and walk without assistance. To ensure age-matching between groups, whenever an older adult was assigned to the kyphosis group based on age criteria, a healthy older adult of the same age was concurrently assigned to the control group. The exclusion criteria were a history of fracture, surgery, or trauma to the spine and lower extremities; inflammatory diseases, such as ankylosing spondylitis, rheumatoid arthritis, central nervous system disorders, neuromuscular disorders, and diabetic neuropathy; untreated hearing or vision disorders; dizziness and vestibular disorders; cardiovascular diseases; T scores < −1 (osteopenia and osteoporosis); joint disease or a visible knee deformity is defined as a gap exceeding 6 cm between the knees (genu varum) or between the ankles (genu valgum); any spinal deformity other than hyperkyphosis; Abbreviated mental test score ≤ 6; and the use of medications that affect the central nervous system or balance. The methods and aims of the assessments were explained to the subjects, and they signed informed consent forms before the assessments. This cross-sectional study was conducted in the Department of Orthotics and Prosthetics of Iran University of Medical Sciences, Tehran, Iran. The study was approved by the ethics committee of Iran University of Medical Sciences (IR.IUMS.REC.1401.180). All methods were performed in accordance with the Declaration of Helsinki. Using G-power software 3.0.1 (Franz Faul, University of Kiel, Kiel, Germany), the sample size was estimated to be at least 64 (n = 32 per group), with α = 0.05, power = 0.8 and an effect size = 0.63 (based on the mean scores and standard deviations of Regolin et al.⁷).

Measurement of kyphosis

Thoracic kyphosis was measured via a Samsung A12 smartphone and the Goniometer-Pro Android app. The participant wore an open-back gown, and the examiner marked the spinous processes of the 1st and 12th thoracic vertebrae (T1 and T12) on the skin. Next, the participants were asked to stand in a relaxed position with their bare feet shoulder width apart, their arms flexed, and fists placed on the clavicles. The Goniometer-Pro program was run first to measure the thoracic kyphosis angle. The center of the bottom edge of the smartphone was placed on the T1 spinous process, and when it displayed an angle of zero degrees, the purple button on the screen was pressed. Next, the phone was removed and placed on the T12 spinous process. The results were displayed by touching the green circle on the phone screen. The lower value was the thoracic kyphosis angle ⁴⁶. Previous studies have reported excellent interrater and intrarater reliability (ICC = 0.89) and good validity (r = 0.81) for the thoracic region⁴⁶.

Measurement of postural control

The participants were asked to stand on a force plate (Kistler-Instrument-AG, Switzerland) in a standard position, with their heels 6 cm apart and their feet externally rotated 10°¹⁶. The force platform data were sampled at 100 Hz. For repeatability, foot placement maps were drawn on the force plate. During the trials, the subjects stood barefoot with their hands hanging in a comfortable position close to their body. Five 60 s eyes-open trials and five 60 s eyes-close (blindfolded) standing trials were performed for each participant⁴⁷. The trial conditions were randomized for each participant. In the eyes-open condition, the participants were asked to look at a piece of black paper mounted on a wall 4 m away at eye level. The participants were asked to breathe normally and avoid talking, coughing, shaking their bodies during the trial, and standing as still as possible. Data recording started 5 s after the subject stood in the intended position on the force platform. To control for fatigue, there was a 60 s break between each trial and a 5 min break between the two conditions.

Stabilogram-diffusion analysis was performed on the CoP trajectories using a custom code developed in MATLAB software (MathWorks Inc., Cambridge, MA, USA). Stabilogram-diffusion analysis quantifies the stochastic and deterministic components of postural sway by analyzing the mean square displacement of the CoP over time intervals, producing a log–log stabilogram-diffusion plot with short-term and long-term regions. The short-term region reflects open-loop control (stochastic muscle activity), while the long-term region reflects closed-loop control (sensory feedback). The analysis focused on the following parameters in the AP, ML, and R directions, where R is the linear sum of AP and ML components:

Short-term Effective Diffusion Coefficient (Ds): The rate of CoP diffusion over short time intervals (< 1 s), calculated as the slope of the short-term region of the stabilogram-diffusion plot. Higher Ds indicates greater stochastic activity in open-loop control, reflecting random muscle movements.

Long-term Effective Diffusion Coefficient (Dl): The rate of CoP diffusion over long time intervals (> 1 s), calculated as the slope of the long-term region. Lower Dl suggests effective closed-loop control, where sensory feedback stabilizes posture.

Short-term Scaling Exponent (Hs): This is the slope of the short-term region in the log–log plot, reflecting the nature of the stochastic process. When Hs is greater than 0.5, past and future increments are positively correlated. In other words, an increasing (or decreasing) trend in the past tends to continue as an increasing (or decreasing) trend in the future, resulting in persistent sway.

Long-term Scaling Exponent (Hl): This is the slope of the long-term region in the log–log plot and is typically less than 0.5, indicating a negative correlation between past and future increments. In this scenario, an increasing (or decreasing) trend in the past is generally followed by a decreasing (or increasing) trend in the future. This anti-persistent sway reflects the action of sensory feedback mechanisms that work to correct deviations.

Critical Time Interval (Ct): The time at which the transition from open-loop to closed-loop control occurs, determined as the intersection of the short-term and long-term regions. It reflects the onset of sensory feedback.

Critical Displacement (Cd): The mean square CoP displacement at Ct, indicating the spatial extent of sway before closed-loop control dominates.

These parameters were derived from the slopes of the linear stabilogram-diffusion plots, as described by Collins and De Luca¹⁶. Additional measures of postural sway included the CoP trajectory range in the AP and ML directions; the mean velocity in the AP, ML, and R directions; and the sway area per unit of time⁴⁸. The values are reported as the average of five trials.

Statistical analysis

The data were analyzed via the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA). Descriptive data analysis and normality tests (Kolmogorov–Smirnov test) were followed by mixed-model ANOVA. The independent variables were group (hyperkyphotic vs. age-matched control) and task difficulty (eyes closed vs. eyes open). The dependent variables were the stabilogram diffusion parameters in the AP, ML, and R x-, y- and r-directions, respectively, i.e., short-term effective diffusion coefficients (Dys, Dxs, and Drs), long-term effective diffusion coefficients (Dyl, Dxl, and Drl), short-term scaling exponents (Hys, Hxs, and Hrs), long-term scaling exponents (Hyl, Hxl, and Hrl), critical time intervals (Cty, Ctx, and Ctr), and critical displacements (Cdy, Cdx, and Cdr), as well as the common postural sway parameters, i.e., average of the AP and ML–CoP ranges, mean velocities (CoPvx, CoPvy, and CoPvr), and sway area per unit time. A p value less than 0.05 was considered statistically significant.

Author contributions

M.H. conceptualized, data analysis, visualization, and manuscript writing. M.J. and R.S. conceptualized, methodologically guided, review and editing, and supervised this work. F.A. conceptualized, methodologically guided, contributed to the statistical analysis, and review and editing. H.GH. conceptualized, methodologically guided, and review and editing. All authors revised and agreed on the final version of the manuscript.

Funding

This work was part of Ph.D. dissertation submitted to Iran University of Medical Sciences and was financially supported by the institution (grant number: 14013424000).

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Ethical approval had been provided by Iran University of Medical Sciences (IR.IUMS.REC.1401.180). All methods were performed in accordance with the Declaration of Helsinki. All experiments were performed in accordance with relevant guidelines and regulations. All participants provided their informed consent prior to inclusion in the study and their data were anonymized to ensure confidentiality.