Postural Sway Assessment in Virtual Reality and Technology-Assisted Rehabilitation: A Systematic Review

Abstract

Postural sway, quantified through center of pressure (COP)-based measures, is a critical indicator of postural stability and fall risk. Despite growing interest in virtual reality (VR) and technology-assisted rehabilitation, a comprehensive synthesis of how interventions and experimental conditions affect postural sway across adults and older adults is lacking. This review aimed to systematically synthesize the effects of exercise-based interventions and experimental physiological, visual, and sensory conditions on COP-based postural sway in adults and older adults, with an emphasis on acute and short-term effects relevant to VR- and technology-assisted rehabilitation. A systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library was conducted from database inception to 31 March 2025. Studies reporting COP-based postural sway outcomes with extractable quantitative data were included. Narrative synthesis was performed due to substantial clinical and methodological heterogeneity. Ten studies were included (2 RCTs, 6 pre–post, 1 crossover, 1 cross-sectional). Exercise-based interventions—including balance training and visual feedback training—generally reduced postural sway. Acute physical perturbations (fatigue, immobilization, cryotherapy, proprioceptive vibration) consistently worsened stability. Destabilizing visual stimulation increased sway, whereas stabilizing visual feedback reduced it. Exercise-based interventions and sensory-stabilizing conditions show promise for improving postural stability. Fatigue, immobilization, and sensory perturbations represent important modifiable risk factors. IMU-based wearable assessment may serve as a clinically scalable alternative to force plate systems in VR-based and rehabilitation settings.

1. Introduction

Postural control—the ability to maintain the body’s center of mass within the base of support—is fundamental to daily functioning, mobility, and independence. It depends on the complex integration of visual, vestibular, and somatosensory inputs, all of which converge to generate appropriate motor responses that preserve balance [1,2,3,4,5]. Alteration, impairment, or disruption of any component of this sensorimotor loop can compromise postural stability and balance performance, thereby contributing to an increased risk of falls [6].

Falls represent a major public health burden, particularly among older adults, contributing to reduced mobility, increased fear of falling, loss of independence, and elevated mortality risk [7]. Increased postural sway—a commonly used surrogate measure of standing balance—has been independently associated with prospective fall incidence in community-dwelling elderly individuals [8].

Recent clinical studies have demonstrated that technology-assisted interventions, including horse-riding simulator exercise [9] and virtual reality-based walking-in-place exercise (VR-WIPE) [10], can significantly improve balance and gait performance in older adults, highlighting the growing potential of technology in balance rehabilitation. Given that postural impairments affect not only older adults but also younger adults with musculoskeletal or neurological conditions, the present review adopted an inclusive approach encompassing adults aged 18 years and over.

Among the tools available for objective balance assessment, center of pressure (COP) analysis is the most widely used method for quantifying postural sway during quiet standing [11]. COP-based variables—particularly sway velocity and sway path length—demonstrate adequate reliability and clinical sensitivity and are widely used as primary outcome measures in postural stability research [12,13,14]. The emergence of inertial measurement unit (IMU)-based wearable devices has further expanded the measurement possibilities: a belt-type IMU device has recently demonstrated high intra- and inter-rater reliability for postural sway assessment [15], suggesting that COP-equivalent measurements are feasible outside of laboratory settings.

Multiple intervention approaches have been applied to improve postural control, including balance training, visual feedback training, core muscle activation programs, and proprioceptive or somatosensory training approaches [16]. Postural alignment variables such as the craniovertebral angle and scapular index have been shown to correlate significantly with proprioception and balance control [17], while ankle range of motion and lower-extremity muscle strength are significant predictors of static postural stability [18], collectively underscoring the multifactorial nature of factors influencing postural sway.

Despite this growing body of evidence, existing systematic reviews have largely focused on specific intervention types or populations [19,20,21]. A comprehensive synthesis that simultaneously examines exercise-based interventions alongside sensory perturbations, acute physiological manipulations, and experimental visual conditions—factors directly relevant to VR and technology-assisted rehabilitation—has been lacking. The present study addresses this gap by integrating diverse intervention types and experimental conditions within a single systematic framework, with particular attention to findings pertinent to VR-based and technology-assisted balance rehabilitation.

The objective of this review was to systematically examine the effects of exercise-based interventions and experimental physiological, visual, and sensory conditions on COP-based postural sway in adults and older adults, and to synthesize their relevance to VR- and technology-assisted rehabilitation.

2. Materials and Methods

2.1. Study Design

The review question was developed according to the PCC (Population, Concept, Context) framework. The Population comprised adults and older adults; the Concept focused on COP-based postural sway outcomes; and the Context encompassed exercise-based interventions and experimental physiological, visual, and sensory conditions relevant to VR- and technology-assisted rehabilitation. Accordingly, this review addressed the following question: “How do exercise-based interventions and experimental physiological, visual, and sensory conditions influence COP-based postural sway in adults and older adults, and what is their relevance to VR- and technology-assisted rehabilitation?” This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [22] and was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD420261384156). The review process comprised literature searching, eligibility screening, data extraction, methodological quality assessment, and narrative synthesis.

2.2. Literature Search Strategy

A systematic search was conducted across four electronic databases—PubMed, Scopus, Web of Science, and the Cochrane Library—by two reviewers (J.-S.O. and S.-G.K.), covering records from database inception through 31 March 2025. Search terms combined MeSH headings and free-text keywords using Boolean operators (AND, OR):

• “postural sway” OR “center of pressure” OR “COP” OR “postural control”;
• “sway length” OR “sway velocity” OR “sway area” OR “trajectory length”;
• “intervention” OR “training” OR “exercise” OR “stimulation” OR “condition” OR “experimental”;
• “adults” OR “older adults” OR “elderly” OR “aging”.

Manual reference searching of the bibliographies of all included articles was performed to identify additional eligible studies. The detailed search strategy for each database is provided in Supplementary Material (Figure S1).

2.3. Eligibility Criteria

Studies were included if they met all of the following criteria: (1) Study design: randomized controlled trials (RCTs), pre–post studies, crossover studies, experimental studies, or cross-sectional comparative studies; (2) Participants: adults aged 18 years or older, including both healthy adults and older adults; (3) Intervention or condition: any exercise-based intervention, sensory stimulation, acute physiological manipulation, or experimental condition capable of influencing postural sway; (4) Outcome measures: at least one COP-based postural sway index (sway length, velocity, area, or displacement); (5) Data availability: sufficient quantitative data (means, standard deviations, sample sizes) to determine the direction of effect; (6) Bibliographic completeness: full bibliographic information (authors, year, journal, DOI) was verifiable.

Studies were excluded if they: (1) did not report COP-based postural sway outcomes; (2) were review articles, protocols, conference abstracts, or case reports; (3) provided insufficient statistical data; (4) had incomplete or unverifiable bibliographic information; or (5) were published in a language other than English.

2.4. Data Extraction

Two reviewers independently (J.-S.O. and S.-G.K.) extracted data from all included studies using a standardized form. Extracted items included: first author, publication year, study design, participant characteristics (age, health status), sample size, intervention type and comparator condition, measurement instrument, outcome variables, time points, and direction of effect. Discrepancies were resolved through discussion between the two reviewers (J.-S.O. and S.-G.K.) until consensus was reached.

2.5. Methodological Quality and Risk of Bias Assessment

Methodological quality was evaluated according to study design. Randomized controlled trials were assessed using the Cochrane Risk of Bias 2 (RoB 2) tool [23]. Non-randomized studies were evaluated with reference to key ROBINS-I domains [24], applying assessments across selection bias, performance bias, detection bias, attrition bias, and reporting bias. Each domain was rated as low risk, some concerns, or high risk, and an overall risk-of-bias judgment was made by integrating domain ratings. Methodological quality and risk of bias were independently assessed by two reviewers (J.-S.O. and S.-G.K.), with any disagreements resolved through discussion until consensus was reached.

2.6. Data Synthesis

Owing to substantial heterogeneity in study designs, intervention types, participant characteristics, and outcome measures, a formal meta-analysis was not feasible. Instead, narrative synthesis was conducted in accordance with the SWiM (Synthesis Without Meta-analysis) guideline [25]. Results were organized by outcome variable type (sway velocity, sway length, RMS sway) and by intervention or condition category (exercise-based, acute physiological perturbation, visual/sensory condition, IMU-based measurement). The direction of effect for each study was determined based on reported means or effect estimates.

3. Results

3.1. Study Selection

The database search retrieved 926 records. After removal of 248 duplicates, 678 records underwent title and abstract screening, of which 573 were excluded. Full-text retrieval was attempted for 105 reports; 20 reports could not be retrieved, leaving 85 reports for full-text eligibility assessment. Of these, 75 were excluded based on pre-specified eligibility criteria. The 75 reports excluded after full-text assessment were mainly excluded because they did not report COP-based postural sway outcomes (n = 27), did not examine an eligible intervention or experimental condition (n = 17), or did not meet the population-related eligibility criteria (n = 31). Ten studies met all inclusion criteria and were included in the qualitative synthesis. The complete flow of study selection is presented in Figure 1.

The included studies encompassed adults and older adults across a range of health statuses. Two were RCTs, six were pre–post designs, one was a crossover study, and one was a cross-sectional comparative study. The most frequently reported outcome was sway velocity (n = 6), followed by sway length or trajectory length (n = 2) and RMS sway (n = 2).

3.2. Overview of Included Studies

Participant populations varied across studies, including community-dwelling older adults with balance deficits, healthy adults, adults with non-specific low back pain, and older adults classified by fall history. Interventions and conditions spanned progressive balance training, transverse abdominis training, visual feedback training, cold water immersion, fatigue induction, lower limb immobilization, peripheral optic flow exposure, proprioceptive vibration, and IMU-based sway measurement. Characteristics of all included studies are summarized in

Table 1. Characteristics of Included Studies.

Author	Year	Design	Population	Intervention/Condition	Comparator	Outcome Measure	N	Result Direction
Sörlén et al. [26]	2021	RCT	Older adults with postural instability	4-week progressive balance training (3×/wk)	No intervention	Sway length (EO/EC)	IG:22/CG:29	trend; no significant between-group effect
Fukuchi et al. [27]	2014	RCT	Healthy adults	20-min cryotherapy (cold water immersion)	Lukewarm water	Sway velocity	IG:13/CG:13	↑ velocity (worsened post-cryotherapy)
Ruhe et al. [28]	2012	Pre–post	Adults with non-specific low back pain	Pain treatment (physical therapy)	None (pre vs. post)	Sway velocity	16	↓ velocity post-treatment
Ferraro et al. [29]	2019	Pre–post	Adults across age groups	Transverse abdominis (TA) training (4 wks)	None (pre vs. post)	Sway velocity	18	↓ velocity post-training
Sotirakis et al. [30]	2020	Pre–post	Healthy adults	Visual feedback (visual target matching)	None (pre vs. post)	Sway velocity	25	↓ velocity with matched visual condition
Gebel et al. [31]	2022	Pre–post	Healthy young adults	Physical & mental fatigue induction	None (pre vs. post)	Sway velocity	15	↑ velocity post-fatigue (worsened)
Ikeda et al. [32]	2022	Pre–post	Healthy adults	10-h lower limb immobilization	None (pre vs. post)	Trajectory length	22	↑ trajectory length after immobilization
Kongsawasdi et al. [33]	2024	Cross-sectional	Older adults (fallers vs. non-fallers)	IMU-based postural sway measurement	Non-fallers	RMS sway	F:25/NF:25	↑ RMS in fallers vs. non-fallers
Pollind & Soangra [34]	2020	Pre–post	Healthy adults	Proprioceptive manipulation (subthreshold plantar vibration)	No vibration	RMS sway	10	↑ RMS with proprioceptive perturbation
Kim & Park [35]	2016	Crossover	Healthy adults	Peripheral optic flow (moving crowd stimulus)	Static visual condition	Sway velocity	20	↑ velocity with optic flow stimulus

Note: ↑ and ↓ indicate an increase and decrease in the corresponding outcome measure, respectively.

.

3.3. Postural Sway Outcome Measures

Sway velocity was the most frequently reported outcome, appearing in six of the ten included studies, followed by sway length or trajectory length (n = 2) and root mean square (RMS) sway (n = 2). Because RMS-based indices differ from length- and velocity-based measures in both unit and clinical interpretation, they were classified as a separate outcome category. The distribution of outcome measures and the direction of findings across categories are presented in

Table 2. Summary of Postural Sway Outcome Measures by Category.

Outcome Category	Studies Reporting	Direction of Findings	Interpretation
Sway Length/Trajectory Length	Sörlén 2021 [26]; Ikeda 2022 [32]	↓ after balance training; ↑ after immobilization	Intuitive and responsive indicator; design heterogeneity limits pooling
Sway Velocity	Fukuchi 2014 [27]; Ruhe 2012 [28]; Ferraro 2019 [29]; Sotirakis 2020 [30]; Gebel 2022 [31]; Kim & Park 2016 [35]	↓ with TA training, visual feedback; ↑ with fatigue, cryotherapy, optic flow	Most frequently reported; highly sensitive to diverse stimuli
RMS Sway	Kongsawasdi 2024 [33]; Pollind & Soangra 2020 [34]	↑ in fallers vs. non-fallers; ↑ with perturbation	Reflects fall-risk classification and sensory perturbation responses

Note: ↑ and ↓ indicate an increase and decrease in the corresponding outcome measure, respectively.

.

3.4. Synthesis of Intervention Effects

Exercise-based interventions—including balance training, core muscle training, and visual feedback training—generally showed a tendency to reduce postural sway. Sway velocity decreased following transverse abdominis training [29], visual feedback training [30], and progressive balance training [26], though the between-group effect in the latter RCT was not statistically significant, suggesting that four weeks of training may be insufficient for robust improvements. Peripheral optic flow stimulation increased sway velocity [35]. All four studies examining acute physiological perturbations—cold water immersion [27], fatigue induction [31], lower limb immobilization [32], and proprioceptive vibration [34]—reported increased postural sway. A synthesis of intervention categories and their effects is provided in

Table 3. Synthesis of Intervention Effects by Intervention Type.

Intervention Category	Studies	Intervention/Condition	Effect	Narrative Interpretation
Exercise/Training	Sörlén 2021 [26]; Ferraro 2019 [29]; Sotirakis 2020 [30]	Balance training, TA training, visual feedback training	↓ sway	Exercise-based interventions generally showed a tendency to reduce postural sway. The marginal between-group effect in Sörlén et al. (2021) [26] suggests that 4 weeks may be insufficient for significant improvement.
Sensory/Visual Stimulation	Kim & Park 2016 [35]	Peripheral optic flow (moving crowd stimulus)	↑ sway	Destabilizing visual input increased sway, demonstrating that the quality and stability of visual information critically determines its effect on postural control.
Acute Physical/Sensory Perturbation	Fukuchi 2014 [27]; Gebel 2022 [31]; Ikeda 2022 [32]; Pollind & Soangra 2020 [34]	Cryotherapy; fatigue induction; limb immobilization; proprioceptive vibration	↑ sway (all)	All acute perturbation conditions worsened postural stability, identifying key destabilizing factors with direct relevance to fall prevention counseling.
Measurement/Risk Classification	Kongsawasdi 2024 [33]	IMU-based sway measurement in fallers vs. non-fallers	↑ RMS in fallers	Supports IMU-based postural sway assessment as a clinically feasible tool for fall-risk classification.

Note. ↓ = reduction in sway (improvement in postural stability); ↑ = increase in sway (deterioration in postural stability). Outcome measures and measurement conditions varied across studies; readers are advised to consult original reports for complete details.

.

3.5. Methodological Quality and Risk of Bias

Risk-of-bias results are summarized in

Table 4. Risk of Bias Summary of Included Studies.

Study	Design	Selection Bias	Performance Bias	Detection Bias	Attrition Bias	Reporting Bias	Overall Risk
Sörlén et al. (2021) [26]	RCT	Low	Some concerns	Some concerns	Low	Low	Some concerns
Fukuchi et al. (2014) [27]	RCT	Low	Some concerns	Some concerns	Low	Some concerns	Some concerns
Ruhe et al. (2012) [28]	Pre–post	High	High	Some concerns	Low	Some concerns	High
Ferraro et al. (2019) [29]	Pre–post	High	High	Some concerns	Low	Some concerns	High
Sotirakis et al. (2020) [30]	Pre–post	High	High	Some concerns	Low	Some concerns	High
Gebel et al. (2022) [31]	Pre–post	High	High	Some concerns	Low	Low	High
Ikeda et al. (2022) [32]	Pre–post	High	High	Some concerns	Low	Low	High
Kongsawasdi et al. (2024) [33]	Cross-sectional	Some concerns	N/A	Some concerns	N/A	Some concerns	Some concerns
Pollind & Soangra (2020) [34]	Pre–post	High	High	Some concerns	Low	Low	High
Kim & Park (2016) [35]	Crossover	Some concerns	Some concerns	Some concerns	Low	Low	Some concerns

Note. Low = low risk (green); some concerns = moderate risk (yellow); high = high risk (red); N/A = not applicable.

. Both RCTs [26,27] were rated as ‘some concerns’ due to incomplete reporting of randomization and allocation concealment procedures. Pre–post studies (n = 6) were predominantly rated as ‘high risk’ owing to the absence of a control group. The crossover study [35] was rated ‘some concerns’ because of limited reporting on condition order randomization. The cross-sectional study [33] was also rated ‘some concerns’, reflecting inherent limitations in causal inference. Overall, the methodological quality of included studies ranged from some concerns to high risk of bias, a limitation that should be considered when interpreting findings.

4. Discussion

This systematic review examined how exercise-based interventions and experimental physiological, visual, and sensory conditions influence COP-based postural sway in adults and older adults, with particular attention to their relevance to VR- and technology-assisted rehabilitation. The synthesized findings largely supported the anticipated direction of effects: exercise-based and visual feedback interventions tended to reduce postural sway, whereas acute physiological perturbations and destabilizing visual input generally increased sway. This pattern aligns with prior evidence that the quality of sensory and visual input can either support or compromise postural stability [36,37,38]. Notably, the present review extends beyond conventional exercise intervention reviews by incorporating sensory and experimental conditions, offering a more comprehensive framework for understanding postural sway as both an outcome variable and a sensitive biomechanical indicator in rehabilitation contexts.

A key finding of this review is that postural sway responds not only to therapeutic exercise but also to a broad range of internal and external conditions—including sensory perturbation, fatigue, proprioceptive manipulation, and visual environment—underscoring its value as a dynamic, multi-sensitive indicator of postural system vulnerability. Exercise-based interventions consistently showed tendencies to reduce sway velocity or sway length, whereas acute perturbation conditions produced the opposite pattern. This bidirectional sensitivity supports the use of postural sway not only as a rehabilitation outcome measure but also as a biomarker for monitoring the risk state of the postural control system [8,39,40,41,42].

The beneficial effects of exercise-based interventions may be attributed to improvements in neuromuscular control, sensory reweighting processes, and lower-extremity and trunk stability [43,44,45]. Balance training and core muscle training enhance trunk–limb coordination during postural tasks, while visual feedback training strengthens error detection and corrective motor responses. Evidence from a randomized controlled trial combining balance training with VR-based visual input manipulation demonstrated reduced COP sway speed under eyes-closed conditions, decreased visual weighting, and altered non-visual feedback loop gain—providing direct mechanistic support for sensory reweighting as a training outcome [46]. This mechanism is particularly relevant to VR-based rehabilitation, in which visual environments can be systematically manipulated to challenge and retrain the sensory integration process. In older adults, a program combining sensory-challenge balance training with vibrotactile sensory augmentation yielded significant improvements in Sensory Organization Test composite scores, with retention at six months [47], confirming the durability of training effects mediated by sensory reweighting. Furthermore, error amplification visual feedback training in older adults produced greater error reduction rates (−46.5% vs. −27.1%) and enhanced multi-segmental coordination [48], identifying augmented error feedback as a potent mechanism for VR-based balance training. These findings collectively highlight the role of technology-assisted sensory manipulation in optimizing balance rehabilitation outcomes, particularly for fall prevention in older adults [47,48].

Conversely, acute physiological perturbations—including muscle fatigue, lower limb immobilization, cold water immersion, and proprioceptive vibration—all worsened postural stability, suggesting that these conditions compromise the efficiency of sensorimotor integration. These destabilizing effects carry important clinical implications: older adults and individuals with impaired balance may be at heightened fall risk in the presence of fatigue accumulation, sensory degradation, or environmental perturbations [49,50,51,52]. Research demonstrating that vestibular training with weight-shifting promotes multisensory adaptation and postural control [53] further implies that proprioceptive disruption may conversely impair this adaptive capacity. Age-related decline in lower-extremity proprioceptive acuity has also been independently associated with reduced postural control efficiency and increased sway [54], underscoring the importance of preserving sensory function. Collectively, these findings support the use of postural sway assessment not only to evaluate rehabilitation outcomes but also as a monitoring tool for detecting early functional vulnerability arising from fatigue, sensory loss, or destabilizing environmental stimuli.

Opposing effects were observed across visual conditions: peripheral optic flow increased sway, while visual feedback training decreased sway velocity. This contrast reflects the critical role of visual input quality in postural regulation and is consistent with the proposed mechanism of visual-vestibular conflict as a driver of postural instability [36,37,38]. These findings are directly relevant to the design of VR rehabilitation environments, where the visual flow parameters must be carefully calibrated to promote stabilization rather than destabilization. Sway velocity emerged as the most frequently reported and arguably the most sensitive postural outcome variable in this review. However, considerable heterogeneity in measurement protocols—including stance configuration, eyes-open vs. eyes-closed conditions, duration, and equipment—limits direct comparison across studies. Future research should prioritize standardization of COP measurement protocols to facilitate quantitative synthesis [12,14,55].

IMU-based wearable devices represent a promising complementary approach to traditional force plate assessment, overcoming spatial and cost constraints that limit laboratory-based measurement [56,57,58,59]. The inclusion of IMU-based studies in the present review—alongside recent evidence supporting the reliability of a belt-type IMU device for postural sway assessment [15]—indicates that sway evaluation is feasible in community and clinical settings beyond the laboratory. In the context of technology-assisted rehabilitation, IMU-based wearable assessment may provide a clinically scalable alternative to force plate systems, supporting real-time balance monitoring in VR-based and technology-assisted rehabilitation programs. Future research should validate the correspondence between COP-based and IMU-derived sway indices, establish minimal detectable change values, and develop standardized field assessment protocols.

From a practical perspective, the bidirectional sensitivity of postural sway supports its use not only as a rehabilitation outcome but also as a monitoring indicator of balance-related vulnerability in clinical and community settings. Exercise-based interventions and stabilizing feedback conditions may be used to reduce excessive sway, whereas increased sway under fatigue, sensory degradation, or destabilizing visual environments may help identify conditions in which postural stability is compromised. In VR- and technology-assisted rehabilitation, IMU-based wearable assessment may provide a scalable and clinically feasible complement to force plate systems, supporting balance monitoring outside laboratory settings. Future experimental studies should include adequately powered randomized controlled trials with appropriate control groups, standardized COP measurement protocols, validation of the correspondence between COP- and IMU-derived sway indices, establishment of minimal detectable change values, and long-term follow-up assessments to determine the durability of intervention effects.

Several limitations of this review should be acknowledged. First, the small number of included studies (n = 10) and the small sample sizes of individual studies limit the generalizability of the findings. Because the included studies varied substantially in population, intervention type, and sway outcome measures, a single required sample size cannot be specified from the present evidence; future trials should therefore perform a priori sample size calculations based on expected changes in COP-based sway outcomes and minimal detectable change values.

Second, substantial heterogeneity in study designs, outcome measures (sway velocity, sway length, RMS), and measurement conditions precluded quantitative meta-analysis. Third, the exclusion of five studies due to unverifiable bibliographic information may have introduced selection bias. Fourth, restriction to English-language publications may have introduced publication bias. Fifth, the predominance of non-randomized designs limits the overall level of evidence.

In summary, the findings of this review demonstrate that postural sway is a sensitive, multifactorial indicator of postural control that responds differentially to therapeutic exercise, sensory perturbation, physiological stress, and visual conditions. Understanding both stabilizing and destabilizing influences on postural sway is essential for designing effective balance rehabilitation strategies, particularly in the context of VR-based and technology-assisted rehabilitation.

5. Conclusions

This systematic review examined the effects of diverse interventions and experimental conditions on postural sway in adults and older adults, with relevance to virtual reality and technology-assisted rehabilitation. Exercise-based interventions—including balance training, core muscle training, and visual feedback training—consistently showed a tendency to reduce postural sway, supporting their role in improving postural stability. In contrast, acute physiological perturbations such as fatigue, lower limb immobilization, cold water immersion, and destabilizing visual stimulation increased postural sway, confirming that postural control is sensitive to a broad range of modifiable internal and external factors.