Physical Inactivity and Sedentary Behavior Negatively Impact Postural Balance and Gait

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This study presents the influence of physical activity–sedentary behavior (PA–SB) interplay on balance and gait performance in healthy young adults. The findings demonstrate that not only physical activity levels but also sedentary behavior patterns modulate postural balance and anticipatory postural adjustments. These insights have direct applications for integrating the PA–SB framework in workplace ergonomics, general physical and mobility health programs, and digital health monitoring devices. Additionally, the findings can be applied to early screening and personalized balance and mobility training protocols to prevent long-term neuromuscular deconditioning and promote lifelong mobility health.

Abstract

Background/Objectives: The benefits of physical activity (PA) do not depend on the PA level alone but also on sedentary behavior (SB). The interaction between PA and SB (i.e., PA–SB interplay) is important to determine one’s health status. This study explored the effect of PA–SB interplay on balance and gait in healthy young adults. Methods: Healthy young adults (n = 133, 18–35 yrs) were placed in four PA–SB interplay groups (according to their sitting duration and physical activity duration) using the American College of Sports Medicine PA guidelines (i.e., sedentary active [>6 h/day, >150 min/week], sedentary inactive [>6 h/day, <150 min/week], physically active [<6 h/day, >150 min/week], and physically inactive [<6 h/day, <150 min/week]). In this cross-sectional study, participants’ balance and gait were assessed with inertial measurement units placed on seven bodily sites. In this exploratory study, significance level was set at p < 0.1. Results: Sway acceleration RMS during the eyes closed on stable surface balance test showed a statistically significant difference among the PA–SB interplay groups (p = 0.055) which was found between sedentary active and physically inactive (p = 0.066). Anticipatory postural adjustment (APA) duration during gait showed a statistically significant difference (p = 0.010) which was found between sedentary inactive and physically active (p = 0.019) and between sedentary active and physically active (p = 0.026). Conclusions: PA–SB interplay influences static (sway acceleration RMS) and dynamic (APA duration) balance of healthy young adults. Findings suggest that somatosensory processing during balance and gait initiation are significantly impacted by PA–SB interplay.

1. Introduction

Broadly defined, physical activity (PA) involves body movements coordinated by skeletal muscles in culturally specific environments and contexts, influenced by interests, ideas, instructions, and relationships [1,2]. This can either be time set aside to perform exercises or during activities of daily living. The health benefits of PA are apparent across lifespan and include improved bone health, weight status, physical function, and cognitive function in children and youth, and improved cognitive function, reduced anxiety and depression risk, improved sleep, and reduced risk of fall-related injuries for older adults [3]. The consequences of inadequate PA are thus significant and are estimated to contribute to up to $117 billion (U.S.) in annual health care costs and to about 10% of premature, preventable deaths [4,5,6].

To obtain benefits of PA, current guidelines recommend that adults engage in 150–300 min of moderate-intensity (50–70% of maximum heart rate) aerobic PA per week or 75–150 min of vigorous-intensity PA (70–85% of maximum heart rate). Comparable benefits can also be achieved from equivalent combinations of moderate- and vigorous-intensity aerobic activity per week [7]. However, the benefits of PA depend not only on an individual’s level of PA but also on the magnitude of sedentary behavior (SB) [8], which is defined as any behavior that leads to up to 1.5 metabolic equivalent of energy expenditure (MET) while sitting or reclining [9]. Sedentary behavior, therefore, interacts with PA to negatively impact the benefits of PA [8,10]. Evidence also suggests that PA may not always compensate for the deleterious effects of SB due to suboptimal gait variability, especially in the mediolateral direction, linked to prolonged SB. These gait alterations are likely influenced by diminished cognitive functions, such as attentional control, executive processing, and dual-task coordination, as well as reduced sensorimotor integration, which together compromise postural adaptability and neuromotor control. [11,12]. For example, a stroke patient who meets the recommended amount of PA at the rehabilitation center may have difficulty maintaining being physically active at home, thus defeating the PA gains. The overall goal is to decrease SB and at least maintain the recommended PA level. Given this physical activity–sedentary behavior (PA–SB) interplay, the American College of Sports Medicine (ACSM) [6,7,13,14] has assigned PA into four categories based on limits of 150 min per week of PA and 6 h per day of SB.

Physical activity does not only affect an individual’s overall health status. Evidence suggests that PA influences postural balance and gait [7,8,9,10,11]. Healthy older adults who engaged in both high and light PA three times a week for 12 weeks showed significant balance improvements with decreased postural sway as assessed by eyes-closed postural conditions [10]. Healthy young adults who engaged in moderate-vigorous PA with less SB showed significantly lower sway area in static balance [15]. Similarly, PA (at least 150 min/week) improved adaptability in gait, allowing individuals to adjust faster and more efficiently to new movement conditions compared to their young adult’s cohort who were physically active less than 150 min/week [16], suggesting that higher PA was associated with better balance control during gait. However, these studies were not designed to rigorously assess the contributions of SB on balance and gait. This project was intended to identify the foundational results needed to demonstrate the influence of SB and PA on an individual’s balance and gait.

We aimed to explore how balance and gait parameters vary among healthy young adults based on their physical activity and sedentary behavior (PA–SB) interplay categories. Our approach began by identifying the specific dimensions of balance and gait most likely to be influenced by PA–SB interplay. We then examined which parameters showed significant differences across PA–SB groups. Given the well-documented negative impact of prolonged sedentary behavior on physical activity, we hypothesized that individuals with high levels of sedentary behavior would demonstrate poorer balance and gait performance compared to those with lower sedentary behavior, regardless of their physical activity levels.

2. Materials and Methods

2.1. Participants

Healthy young adults were recruited. A short 10-question survey containing items about neurologic, musculoskeletal, visual, or wound problems was used to screen participants for eligibility. Participants who could stand and walk for 2 min without an assistive device were selected. Participants with any neurological condition, recent orthopedic surgery (within 6 months), or sensation impairments that would impact balance and walking ability were excluded. Using a convenient sampling method for this exploratory study, one hundred and thirty-three healthy young adults provided written consent to participate in the study approved by Clarkson University IRB (Approval #18.39.1; 19 June 2020).

2.2. Study Design

A cross-sectional study design was used to assess balance and gait measures. Participants also completed a physical activity questionnaire [13].

2.3. Outcome Measures and Assessment Procedure

The following outcome measures were collected for all participants:

• Physical activity and sedentary behavior: The International Physical Activity Questionnaire (IPAQ) [13], a 27-item self-reported questionnaire, was used to assess participants’ physical activity levels pertaining to time spent on work, transportation, home activities, recreation, sport, leisure, and sitting in a typical week.
• Body motion (using inertial measurement units): The APDM monitoring inertial sensor system (APDM Inc., Portland, OR, USA) [17,18,19], a wireless wearable device for comprehensive analysis of balance and gait, was used to assess body motions. The device’s sensors were attached to seven anatomical sites: forehead (middle of the frontal bone, approximately 2.5 cm above the nasal bone), sternum (body of sternum superior to the xyphoid process), fifth lumbar vertebra, left and right wrists (immediately superior to the radio-ulnar joint), and left and right first feet (on the metatarsals, directly superior to the metatarsophalangeal joint). These sensors were used to collect and analyze balance and gait parameters, resulting in different dimensions of 33 and 194 variables, respectively.
• Static balance: The modified clinical test of sensory interaction on balance (MCTSIB) was used to assess participants’ balance using the Airex foam pad (Airex AG, Sins, Switzerland). This test identifies the sensory contributions of visual, somatosensory, and vestibular systems for postural balance. The MCTSIB has four conditions: (1) eyes open (EO) on stable support (SS); (2) eyes closed (EC) on SS; (3) EO on foam surface (FS), and (4) EC on FS [18]. Balance was assessed for each condition for 30 s with the APDM sensors in situ as the participants stood upright as stable as possible with arms by their sides.

2.4. Procedure

Participants completed the IPAQ on a tablet using questions programmed on surveymonkey.com to obtain their PA and SB levels. In a seated position, the seven anatomical sites were located and marked, and the APDM mobility sensors were affixed to these sites using its velcro straps. With the APDM sensors in place, participants first completed the MCTSIB barefoot, followed by a two-minute walking task along a 6 m track in a physiology laboratory while wearing comfortable athletic footwear. Participants were not given a practice period and data was collected between 10 a.m. and noon. At the beginning of the walking task, participants were instructed to begin walking at a comfortable pace whenever they felt ready.

2.5. Data Management and Analysis

The questions and responses of the IPAQ were extracted and entered into a spreadsheet. Data on the APDM monitors were extracted, and balance (including sway area, sway velocity, sway acceleration, and sway jerk) and gait parameters (including speed, cadence, step length, stride length, turn velocity, double support, and anticipatory postural adjustment) were obtained. The APDM system outputs various dimensions of the same parameter, resulting in 33 balance and 194 gait parameters. For instance, a balance parameter such as sway area alone during the MCTSIB has various dimensions including sway area rotation, sway area in the coronal plane, and ellipse sway area. Each of these dimensions has the four conditions of the MCTSIB (i.e., eyes open on stable surface, eyes closed on stable surface, eyes open on foam surface, and eyes closed on foam surface). We then implemented elastic net regression [20,21] to find the most relevant balance and gait factors (parameters) in this study. Elastic net regression is valuable for handling our complex datasets with multicollinearity and high dimensionality while maintaining model stability and performance. It helps reduce the impact of multicollinearity while keeping the most important variables. Since the variables in our dataset are highly correlated, this method identified seven relevant postural balance measures and seven gait parameters (Figure 1).

Furthermore, three gait parameters—gait cycle, stance, and swing duration—that the elastic net regression approach did not identify but were found to be relevant in the literature were added, resulting in 10 gait parameters [10] (

Table 1. Relevant postural balance and gait parameters.

Postural Balance	Gait
Sway pathlength during eyes closed on foam surface	Stride length
Sway RMS during eyes closed on stable surface	Gait speed
Sway velocity during eyes open on stable surface	Double support duration
Sway jerk during eyes closed on stable surface	Turn velocity
Sway jerk during eyes open on foam surface	Anticipatory postural adjustment duration
Sway jerk during eyes closed on foam surface	Arm swing velocity
Trunk/lumbar rotation during eyes open on foam surface	Asymmetry toe out angle
	Stance duration
	Swing duration
	Gait cycle duration

).

The Mahalonobis distance technique 21 was used to identify multivariate outliers in the analysis (with p-value < 0.05). The Mahalonobis distance technique is used to identify outliers while considering multiple variables at the same time. In this case, the postural balance and gait parameters after the elastic net regression were included as variables of the study to detect multivariate outliers. The predictor variable (i.e., PA–SB interplay) was then divided into four categories according to the American College of Sports Medicine (ACSM) guidelines: (1) sedentary active (high SB and high PA, >6 h/day and >150 min/week), (2) sedentary inactive (high SB and low PA, >6 h/day and <150 min/week), (3) physically active (low SB and high PA, <6 h/day and >150 min/week), and (4) physically inactive (low SB and low PA, <6 h/day and <150 min/week) [6,7,13,14]. Statistical analysis was divided into two components. First, we investigated the association between PA–SB interplay and postural balance measures, and second, the association between PA–SB interplay and gait parameters using one-way ANOVA [22,23]. The assumption of normality was assessed and met, indicating that the residuals within each group follow a normal distribution, which validates the use of ANOVA [23]. PA–SB interplay was used as a predictor variable for both postural balance and gait parameters, and each of the specified postural balance and gait parameters was used as a response variable independently.

Tukey’s post hoc test statistic was employed to compare groups when a statistical difference had been identified between them. In addition, one-way ANOVA tests were employed to identify if statistical differences exist in participants’ demographics. All statistical analyses were conducted using Minitab software (version 21.1) and R Statistical software (4.4.1). Given the exploratory nature of this study and the large number of balance (n = 33) and gait (n = 194) parameters representing various dimensions of the constructs, the significance threshold for the ANOVA analyses examining the association between PA–SB interplay and postural balance and gait outcomes was set at α < 0.10. This was deemed important to reduce the risk of type II errors and allow detection of potential associations between PA–SB interplay and gait/postural outcomes.

3. Results

3.1. Outliers Detection

Six PA–SB interplay group data points, along with their corresponding balance and gait measures, were identified as outliers (p > 0.05) and were excluded from the analysis. Predictor variables were divided into four groups as shown in

Table 2. Number of participants demonstrating duration of physical activity versus duration of sitting.

Physical Activity vs. Sitting Duration	≥150 min/Week (n, %)	<150 min/Week (n, %)
>6 h/day	Sedentary active (28, 22.0%)	Sedentary inactive (35, 27.6%)
≤6 h/day	Physically active (35, 27.6%)	Physically inactive (29, 22.8%)

Sedentary active = high SB–high PA ≥ 6 h/day and >150 min/week; sedentary inactive = high SB–low PA ≥ 6 h/day and <150 min/week; physically active = low SB–high PA ≤ 6 h/day and >150 min/week; physically inactive = low SB–low PA ≤ 6 h/day and <150 min/week; sedentary behavior; PA = physical activity; n = sample size.

. There were no differences in the number of participants in the four categories (p > 0.05). The sedentary inactive (n = 35) and physically active (n = 35) groups had the highest number of participants.

3.2. Demographics

Participants’ mean age was 25.37 ± 7.45 yrs (18–35 yrs), with height and weight of 1.73 ± 0.09 m and 73.56 ± 14.00 kg, respectively. None of the demographic variables showed a statistically significant difference across the groups (

Table 3. Classification of participants into four PA–SB interplay categories.

Variable	Sedentary Active (n = 28)	Sedentary Inactive (n= 35)	Physically Active (n = 35)	Physically Inactive (n = 29)	p-Value
Age (yrs)	24.71 ± 5.94	24.95 ± 7.22	25.17 ± 5.69	26.72 ± 10.49	0.731
Sex (M/F)	10/18	13/22	12/23	10/19	0.995
Height (m)	1.71 ± 0.08	1.74 ± 0.09	1.73 ± 0.08	1.72 ± 0.10	0.931
Weight (kg)	70.39 ± 12.83	74.91 ± 16.55	74.91 ± 12.88	73.37 ± 13.18	0.558
BMI (kg/m2)	24.07 ± 4.02	24.63 ± 4.73	24.93 ± 4.44	24.55 ± 2.80	0.458

Sedentary active = high SB–high PA ≥ 6 h/day and >150 min/week; sedentary inactive = high SB–low PA ≥ 6 h/day and <150 min/week; physically active = low SB–high PA ≤ 6 h/day and >150 min/week; physically inactive = low SB–low PA ≤ 6 h/day and <150 min/week; sedentary behavior; PA = physical activity; n = sample size.

).

3.3. Influence of PA–SB on Postural Control

The sway acceleration root mean squared (RMS) during eyes closed on stable surface (condition 2) showed a statistically significant difference between the four PA–SB interplay (F_3,123 = 2.596; p = 0.055; Figure 2). Post hoc analysis showed that the difference was between sedentary active and physically inactive (high SB–high PA vs. low SB–low PA) (T = 2.49; p = 0.066).

No other balance parameter showed a significant difference among the four PA–SB groups.

Table 4. Relationship between postural balance and PA–SB interplay (X ± SD; CI).

Balance Parameter	All Subjects	Sedentary Active	Sedentary Inactive	Physically Active	Physically Inactive	F Value	p-Value
Sway pathlength coronal (m)
EO-SS	2.51 ± 1.30 2.32–2.70	2.30 ± 0.81 2.08–2.53	2.45 ± 1.17 2.09–2.80	2.44 ± 1.19 2.07–2.81	2.86 ± 1.85 2.35–3.37	0.995	0.397
EC-SS	2.52 ± 1.08 2.36–3.68	2.83 ± 0.88 2.59–3.08	2.57 ± 1.13 2.23–2.91	2.49 ± 0.94 2.20–2.78	2.59 ± 1.39 2.20–2.98	0.136	0.938
EO-FS	3.65 ± 1.26 3.47–3.83	3.64 ± 1.02 3.36–3.92	3.54 ± 1.20 3.17–3.90	3.72 ± 1.24 3.34–4.10	3.70 ± 1.57 3.26–4.13	0.138	0.937
EC-FS	5.63 ± 2.22 5.30–5.95	5.63 ± 1.82 5.12–6.13	5.85 ± 2.69 5.03–6.67	5.45 ± 1.64 4.94–5.96	5.57 ± 2.64 4.84–6.30	0.191	0.902
Sway velocity (m/s)
EO-SS	0.13 ± 0.14 0.11–0.15	0.10 ± 0.04 0.09–0.11	0.14 ± 0.16 0.09–0.19	0.11 ± 0.08 0.09–0.14	0.17 ± 0.22 0.11–0.23	1.694	0.172
EC-SS	0.11 ± 0.06 0.10–0.12	0.10 ± 0.03 0.09–0.10	0.12 ± 0.07 0.10–0.14	0.10 ± 0.04 0.09–0.11	0.13 ± 0.09 0.10–0.16	1.527	0.211
EO-FS	0.14 ± 0.07 0.13–0.15	0.14 ± 0.08 0.11–0.16	0.13 ± 0.05 0.11–0.15	0.13 ± 0.06 0.11–0.15	0.15 ± 0.08 0.13–0.17	0.712	0.547
EC-FS	0.19 ± 0.11 0.17–0.21	0.18 ± 0.08 0.16–0.20	0.18 ± 0.07 0.16–0.20	0.20 ± 0.14 0.16–0.24	0.21 ± 0.12 0.18–0.24	0.517	0.671
Sway RMS acceleration (m/s2)
EO-SS	0.06 ± 0.05 0.05–0.07	0.05 ± 0.01 0.05–0.05	0.07 ± 0.08 0.05–0.09	0.05 ± 0.02 0.04–0.06	0.08 ± 0.07 0.06–0.10	2.010	0.116
EC-SS	0.06 ± 0.02 0.06–0.06	0.05 ± 0.01 0.05–0.05	0.06 ± 0.02 0.05–0.07	0.06 ± 0.01 0.06–0.06	0.07 ± 0.02 0.06–0.07	2.596	0.055 *
EO-FS	0.07 ± 0.02 0.06–0.07	0.07 ± 0.02 0.06–0.08	0.06 ± 0.01 0.06–0.06	0.07 ± 0.01 0.07–0.07	0.07 ± 0.02 0.06–0.07	1.313	0.273
EC-FS	0.11 ± 0.03 0.11–0.11	0.11 ± 0.03 0.10–0.11	0.11 ± 0.03 0.10–0.11	0.11 ± 0.03 0.10–0.12	0.12 ± 0.04 0.11–0.13	0.993	0.399
Sway jerk (sagittal) (m2/s5)
EO-SS	1.73 ± 1.54 1.51–1.96	1.76 ± 4.60 0.48–3.03	1.50 ± 1.21 1.13–1.87	1.70 ± 1.90 1.10–2.29	1.95 ± 1.91 1.41–2.49	1.54	0.478
EC-SS	1.23 ± 1.00 1.08–1.38	1.03 ± 0.64 0.85–1.20	1.42 ± 0.93 1.14–1.70	1.09 ± 0.94 0.80–1.38	1.36 ± 1.35 0.99–1.74	1.19	0.316
EO-FS	1.73 ± 1.54 1.51–1.96	1.77 ± 1.00 1.50–2.05	1.51 ± 1.22 1.14–1.88	1.73 ± 1.86 1.15–2.30	1.95 ± 1.90 1.42–2.48	0.423	0.737
EC-FS	5.03 ± 4.36 4.39–5.67	5.44 ± 4.70 4.13–6.75	4.72 ± 3.20 3.74–5.70	4.41 ± 4.14 3.12–5.70	5.74 ± 5.45 4.23–7.26	0.618	0.604

EO = eyes open; EC = eyes closed; SS = stable surface; FS = foam surface; sedentary active = high SB–high PA ≥ 6 h/day and >150 min/week; sedentary inactive = high SB–low PA ≥ 6 h/day and <150 min/week; physically active = low SB–high PA ≤ 6 h/day and >150 min/week; physically inactive = low SB–low PA ≤ 6 h/day and <150 min/week; sedentary behavior; PA = physical activity; degree of freedom = (3, 109); CI = confidence interval; * p ≤ 0.10.

shows the sway scores and F-statistics of selected balance parameters among the relevant ones identified in the study.

3.4. Influence of PA–SB on Gait

The anticipatory postural adjustment duration (Figure 3) showed a statistically significant difference between the four PA–SB interplay groups (F_3,123 = 3.940; p = 0.010). Post hoc analysis showed that the difference was between sedentary inactive and physically active (i.e., high SB–low PA vs. low SB–high PA, T = 2.96; p = 0.019) and between sedentary active and physically active (i.e., high SB–high PA vs. low SB–high PA, T = 2.85; p = 0.026).

No other gait parameter showed a significant difference between the four PA–SB groups.

Table 5. Relationship between gait and PA–SB interplay (X ± SD; CI).

Gait Parameter	All Subjects	Sedentary Active	Sedentary Inactive	Physically Active	Physically Inactive	F Value	p-Value
Anticipatory postural adjustment duration (s)	0.51 ± 0.26 0.47–0.55	0.44 ± 0.13 0.40–0.48	0.44 ± 0.19 0.38–050	0.64 ± 0.35 0.53–0.75	0.48 ± 0.23 0.42–0.51	3.94	0.010 *
Stride length (std; m)	0.03 ± 0.01 0.02–0.03	0.04 ± 0.01 0.04–0.04	0.03 ± 0.01 0.03–0.03	0.04 ± 0.01 0.04–0.04	0.03 ± 0.01 0.03–0.03	1.37	0.272
Gait speed (std; m/s)	0.05 ± 0.01 0.05–0.05	0.05 ± 0.02 0.04–0.06	0.04 ± 0.01 0.04–0.04	0.05 ± 0.01 0.05–0.05	0.05 ± 0.01 0.05–0.05	0.95	0.421
Gait cycle duration (s)	1.14 ± 0.08 1.13–1.15	1.14 ± 0.08 1.12–1.16	1.14 ± 0.08 1.12–1.16	1.15 ± 0.09 1.12–1.18	1.11 ± 0.08 1.09–1.13	1.32	0.272
Stance duration (std; s)	0.39 ± 0.12 0.37–0.40	0.39 ± 0.10 0.36–0.41	0.42 ± 0.12 0.38–0.46	0.40 ± 0.16 0.35–0.45	0.37 ± 0.09 0.35–0.40	0.69	0.562
Swing duration (std; s)	1.27 ± 0.67 1.17–1.37	1.07 ± 0.46 0.94–1.20	1.39 ± 0.59 1.21–1.57	1.30 ± 0.67 1.10–1.50	1.28 ± 0.75 1.07–1.49	1.42	0.242

Sedentary active = high SB–high PA ≥ 6 h/day and >150 min/week; sedentary inactive = high SB–low PA ≥ 6 h/day and <150 min/week; physically active = low SB–high PA ≤ 6 h/day and >150 min/week; physically inactive = low SB–low PA ≤ 6 h/day and <150 min/week; sedentary behavior; PA = physical activity; degree of freedom = (3, 123), std = standard deviation; CI = confidence interval; * p ≤ 0.10.

shows outcome measures and the F-statistics of selected gait parameters among the relevant ones identified in the study.

4. Discussion

This study investigated the influence of reported physical activity (PA) and sedentary behavior (SB) on postural balance and gait parameters in healthy young adults. Two variables, one each from postural balance and gait parameters, demonstrated significant differences between the PA–SB interplay groups. Our findings suggest that individuals who were sedentary active (high SB–high PA) had significantly better balance (i.e., sway acceleration RMS) than those who were physically inactive (low SB–low PA), while physically active individuals (and not sedentary active, low SB–high PA) had significantly longer postural adjustment during gait initiation compared to those who were sedentary, regardless of PA levels. These findings may help researchers understand the complex interplay between PA and SB as it relates to its impact on balance and gait.

The postural sway acceleration RMS showed significant differences specifically between sedentary active and physically inactive groups. The sway acceleration RMS during eyes closed on stable surface on the MCTSIB depicts the somatosensory integrity used during the balance task, with lower sway values indicating better balance [24,25]. For the gait parameters, anticipatory postural adjustment (APA) duration during forward step turn demonstrated significant difference in the four PA–SB interplay categories, which were found between sedentary active (high SB–high PA) and physically active (low SB–high PA), and sedentary inactive (high SB–low PA) and physically active groups. The APA duration is the period between the end of the first turn and the first step taken during the walking task as participants adjusted their balance and posture to initiate the step. The findings suggest that somatosensory processing during static balance and anticipatory postural adjustments during gait may be impacted by the level of PA–SB interplay among healthy young adults.

4.1. Influence of PA–SB Interplay on Postural Balance

Our findings suggest that during the eyes closed on stable surface condition, there were significant differences in sway acceleration between the sedentary active group (high SB–high PA) and physically inactive group (low SB–low PA) group, with the former exhibiting significantly better balance. Although no significant differences were observed between the sedentary inactive (high SB–low PA) and physically active (low SB–high PA) groups, the latter performed better by magnitude, following the sedentary active group. These findings are interesting both for groups that were significantly different (sedentary active; physically inactive) and those that were not. The high PA in the sedentary active group appeared to compensate for the deleterious high SB, leading to the most favorable balance performance. Conversely, in the physically inactive group, low SB alone did not offset the negative impact of low PA and thus did not translate into better postural control. These findings suggest that there is an interplay between SB and PA and their impact on somatosensory balance control and that one does not uniquely influence balance control when accounting for the other. While moderate PA may confer protective effects on balance control, mitigating the adverse consequences of SB, evidence also shows a negative impact of SB on balance performance [8,10]. Furthermore, light-to-moderate PA can improve postural control [26] by enhancing proprioceptive and vestibular responsiveness. Hence, monitoring SB and engaging in light-to-moderate PA are essential for maintaining efficient postural balance. These findings corroborate previous work suggesting that physically active older adults had improved balance under eyes-closed conditions [10]. However, our findings also suggest that Pau et al. (2014) [10] should have accounted for SB since balance for individuals with low SB (<6 h/day) was not impacted by PA participation.

Despite having different activity profiles, the sedentary inactive group did not differ significantly from the physically active group. This suggests that high levels of PA do not always guarantee superior balance— especially when high sedentary time is also present. Clinicians should therefore consider incorporating behavioral strategies to minimize prolonged SB alongside light-to-moderate PA to optimize balance outcomes. For populations at risk of impaired balance—such as older adults or individuals with mobility limitations—tailored interventions that reduce SB while enhancing PA may be more effective than either strategy alone. Future studies with larger sample sizes and improved statistical power are warranted to determine whether this pattern holds under conditions of reduced variability and enhanced effect sizes in the PA–SB groups.

Although we did not assess the interaction between SB, PA, and underlying biological/physiological factors that impact balance control—such as motor, attentional, and eye tracking systems—we hypothesize that high PA may positively influence the somatosensory system in individuals who sit for extended periods. The somatosensory system has an extensive influence on the balance system by providing the perception of touch in the soles of the feet and proprioception in the joints of the body [27,28]. As the body sways during the balance task, the somatosensory system provides the sense of body joint position and movement to the somatosensory areas of the brain and the brain, in turn, transmits descending motor output to postural muscles to keep the body steady [28,29]. Healthy individuals depend majorly on somatosensory (70%) input when standing on stable support with eyes open, compared to vestibular (20%) and visual (10%) input [29]. Evidence suggests that when visual input is removed during walking on an even firm surface, there is a greater reliance on the somatosensory system. Therefore, the integrity and responsiveness of the somatosensory system are vital for effective balance regulation, especially in situations where other sensory inputs are diminished or unreliable. Clinicians should recognize that balance dysfunction in sedentary individuals may not be a function of motor deficits but also underutilized, desensitized or impaired somatosensory pathways. Rehabilitation strategies should therefore include activities that engage the somatosensory system such as dynamic walking balance tasks over varying surfaces that challenge joint proprioception.

4.2. Influence of PA–SB Interplay on Gait

Our findings suggest that compared to sedentary active and sedentary inactive groups, the physically active group demonstrated a relatively longer APA while performing a walking task on an even terrain. Interestingly, no significant differences were observed between the sedentary active and sedentary inactive groups, nor between physically active and physically inactive groups. These results suggest that a combination of PA and SB influence young adults’ ability to process pre-movement strategies to initiate gait out of a turn.

Depending on the presented condition, a longer or shorter APA duration during gait has advantages and disadvantages. A longer APA duration allows for a more comprehensive activation of postural muscles, which enables the muscles to stabilize the body before shifting weight onto the next step, reducing the risk of falls [30]. The disadvantage of a longer APA duration is slowing down the initiation of the step, leading to reduced overall walking speed, inefficient gait, and slowed reaction time to respond to emergency situations [29], such as walking on a rocky surface or running away from danger. In this current study, participants were instructed to walk at their self-selected pace on an even terrain, suggesting that by demonstrating a relatively longer APA the low SB (i.e., physically active) group stabilized the body while initiating the first step properly. The longer APA duration observed in the physically active group—despite similar gait speeds across groups—suggests improved postural preparation and motor planning, supporting better balance and stability before movement initiation [31]. This enhanced anticipatory control likely results from greater proprioceptive feedback and neuromuscular efficiency developed through regular PA [32]. On the contrary, the shorter APA durations exhibited by the sedentary active and inactive groups reflect the negative effects that SB has on sensorimotor integration, muscle coordination, and anticipatory postural control [33], indicating less time to stabilize the body before movement, which may lead to reduced balance control and increased risk of instability [34].

Generally, low PA (sedentary inactive, physically inactive) may suppress APA responsiveness, regardless of sitting duration. Meanwhile, high PA alone appears to induce greater APA durations—possibly due to enhanced proprioceptive awareness, muscle preactivation strategies, and cortical control developed through consistent movement. This suggests that PA, rather than SB alone, is the primary driver of anticipatory postural engagement. Clinicians should recognize that high PA may serve as a protective factor for efficient APA during dynamic balance tasks. Accordingly, rehabilitation and fall prevention programs should incorporate APA-focused training strategies, such as perturbation-based exercises and step initiation tasks, to improve pre-movement postural control and reduce fall risk.

4.3. Limitations

One limitation is that participants were healthy young adults without impairments that could influence their balance and walking performance. Hence, the difference in balance and gait between the four PA–SB interplay groups was not significant for most of the relevant identified parameters. Another limitation is that we assessed PA and SB using self-report data. There is evidence that suggests that individuals may self-report higher PA on self-reported surveys [35]. The cross-sectional nature of this data is an additional limitation as we were unable to identify whether the differences between the groups were solely based on SB and PA or if there may be other confounding variables that were not assessed in this study. Furthermore, due to the primary aim to explore the interaction between PA and SB, this study did not control for potential confounders such as fatigue and sleep quality, which may influence balance and gait performance. Future research should consider these factors to enhance interpretability of PA–SB interactions.

5. Conclusions

This study demonstrated the influence of physical activity and sedentary lifestyle interplay on balance during static stance and walking. We found the sway acceleration RMS and APA duration to show differences between the four PA–SB interplay groups. Our findings suggest that for balance, high PA may have compensatory effects on high SB; however, our gait findings suggest that individuals who have high PA and low SB are the ones with better postural balance coming out of turns during walking. Our findings support the need to study the interplay between SB and PA instead of SB alone or PA alone. Having found such differences even in healthy young adults, it is imperative to encourage the general population (particularly the older population) and provide educational programs to minimize the level of sedentary behavior (i.e., below 6 h/day) and increase physical activity to a minimum of 150 min per week. However, this assertion should be interpreted with caution. Future studies should investigate how the four PA-SB interplay groups can influence the balance and gait behaviors in elderly and diseased populations.