Functional Fitness and Objectively Measured Movement Behaviors in Relation to Arterial Stiffness in Older Adults: Age Emerges as the Only Independent Correlate After Multivariable Adjustment

Highlights

Public health relevance—How does this work relate to a public health issue?

• Central arterial stiffness is a major predictor of cardiovascular morbidity and mortality in older adults.
• Clarifying the relative contribution of movement behaviors and functional fitness to arterial stiffness may improve cardiovascular risk assessment and healthy aging strategies.

Public health significance—Why is this work of significance to public health?

• This study helps clarify the relative contribution of functional fitness, physical activity, and sedentary behavior to vascular health in older adults.
• The findings suggest that multidimensional functional fitness provides a more integrated indicator of age-related physiological status than movement behaviors alone, with potential value for cardiovascular risk assessment.

Public health implications—What are the key implications or messages for practitioners, policy makers and/or researchers in public health?

• Routine assessment of functional fitness should be considered as a complementary component of cardiovascular risk evaluation in community-dwelling older adults.
• Public health interventions and future research should investigate whether preserving or improving functional fitness can contribute to healthier vascular aging beyond the effects of chronological aging.

Abstract

Background: Central arterial stiffness assessed by carotid–femoral pulse wave velocity (PWVc-f) strongly predicts cardiovascular risk. We examined associations of objectively measured physical activity, sedentary behavior, and functional fitness with PWVc-f in community-dwelling older adults. Methods: The study included 170 older adults (124 women; 64–91 years). Moderate-to-vigorous physical activity (MVPA) and sedentary behavior were assessed by accelerometry, functional fitness by the Fullerton Functional Fitness Test (composite score), and PWVc-f by applanation tonometry. Associations were examined using correlation, hierarchical linear regression, and logistic regression analyses. Results: Elevated arterial stiffness (PWVc-f > 10 m/s) was present in 62.3% of participants. PWVc-f was associated with all functional fitness measures, including the composite score (ρ = −0.338, p < 0.001), but not with MVPA or sedentary behavior. High functional fitness was associated with lower odds of elevated PWVc-f in unadjusted analyses (OR = 0.39, 95% CI 0.20–0.78; p = 0.007), but this was no longer significant after adjustment for age, sex, and anthropometric/clinical characteristics. Neither MVPA nor sedentary behavior predicted PWVc-f. Age remained the only independent correlate across all models. Conclusions: Functional fitness showed stronger unadjusted associations with PWVc-f than MVPA or sedentary behavior. However, after multivariable adjustment including sex and clinical covariates, age emerged as the only consistent independent correlate of PWVc-f. These findings highlight the importance of considering age when interpreting the relationship between functional fitness and arterial stiffness in older adults.

1. Introduction

Arterial stiffening is among the most important age-related vascular changes and independently predicts cardiovascular morbidity and mortality in older adults [1,2,3,4]. Increased central arterial stiffness contributes to impaired ventricular–vascular coupling, elevated cardiac afterload, reduced coronary perfusion, and progressive loss of cardiovascular reserve, thereby linking vascular aging to declines in physical function and healthy longevity [3,4,5]. These alterations are particularly relevant in older populations, in whom aging-related structural and functional changes in the arterial wall are frequently aggravated by hypertension, obesity, physical inactivity, and metabolic disorders [6,7].

Carotid–femoral pulse wave velocity (PWVc-f) is the gold-standard noninvasive measure of central arterial stiffness and a well-established tool for cardiovascular risk assessment in both clinical and epidemiological settings [8,9]. Given the recognized vascular benefits of exercise, increased physical activity has been proposed as a potentially important determinant of arterial health during aging [10,11]. However, evidence relating physical activity levels to arterial stiffness in older adults remains inconsistent. Although some studies and meta-analyses suggest that physically active individuals exhibit lower PWVc-f values, reported effect sizes are generally modest and highly heterogeneous [12,13,14]. Moreover, associations tend to weaken substantially when physical activity is objectively assessed using accelerometry rather than self-reported questionnaires, suggesting that behavioral measures of physical activity may not fully capture the physiological adaptations relevant to vascular aging [13,14].

One possible explanation for these inconsistencies is that the relationship between movement behavior and vascular health may depend not only on the amount of physical activity performed but also on the individual’s integrated functional capacity. In this context, functional fitness may provide complementary information about biological aging and cardiovascular health because it integrates multiple dimensions of physical function rather than reflecting a single behavioral attribute. Functional fitness reflects the integrated interaction among cardiorespiratory capacity, muscular strength, mobility, balance, flexibility, and neuromuscular coordination required to perform daily tasks safely and independently [15,16]. Declines in these domains are associated with frailty, sarcopenia, reduced exercise tolerance, impaired endothelial function, chronic low-grade inflammation, and autonomic dysfunction, all of which may contribute to arterial stiffening [17,18,19,20].

Previous studies have reported inverse associations between isolated physical fitness components, such as cardiorespiratory fitness, muscular strength, gait performance, and flexibility vs. measures of arterial stiffness [11,17,21,22]. However, most investigations have focused on single fitness domains rather than adopting a multicomponent functional approach reflecting overall physiological reserve. This distinction may be clinically important because functional performance in older adults depends on the interaction of multiple physical capacities rather than isolated physiological traits. Whether functional fitness is more strongly associated with arterial stiffness than objectively measured physical activity and sedentary behavior remains to be determined.

Clarifying these relationships has practical implications for cardiovascular prevention strategies in aging populations. Because arterial stiffness is influenced by multiple demographic, clinical, and behavioral factors, including age, adiposity, hypertension, and movement behaviors, evaluating functional fitness within this broader context may improve our understanding of its relationship with vascular aging. If functional fitness exhibits stronger associations with arterial stiffness than behavioral measures of physical activity, it may provide clinically useful information that complements conventional estimates of weekly activity levels or sedentary time. This perspective may also help explain why older adults with similar physical activity levels often exhibit markedly different vascular and functional profiles.

Therefore, the present study investigated the associations of objectively measured physical activity, sedentary time, and multicomponent functional fitness with carotid–femoral pulse wave velocity in community-dwelling older adults, while accounting for established demographic and clinical correlates of arterial stiffness. Based on previous evidence, age, sex, BMI, hypertension status, objectively measured physical activity, and sedentary behavior were identified a priori as established correlates of arterial stiffness and were considered in the multivariable analyses. We hypothesized that functional fitness would exhibit stronger associations with arterial stiffness than accelerometer-derived measures of physical activity and sedentary behavior, reflecting its multidimensional representation of overall physical function.

2. Materials and Methods

2.1. Participants

This cross-sectional observational study included community-dwelling older adults aged ≥ 60 years recruited from outpatient clinics and senior community centers in the city of Porto, Portugal. Eligibility criteria included the ability to perform functional assessments independently and the absence of acute or unstable clinical conditions that limit physical testing. Individuals with severe neurological, musculoskeletal, cardiovascular, or cognitive impairments compromising safe participation in the procedures were excluded.

Sample size estimation was performed a priori using G*Power software (version 3.1.3, University of Kiel, Kiel, Germany), assuming α = 0.05, statistical power (1 − β) = 0.80, and a moderate effect size (f² = 0.15), with a minimum required sample of 84 participants. The a priori sample size calculation was performed for the overall study analyses and was not intended to provide adequate power for sex-stratified analyses or between-sex comparisons. Therefore, analyses comparing women and men should be considered exploratory, particularly given the smaller number of male participants.

Approximately 400 community-dwelling older adults were invited to participate through outpatient clinics and senior community centers, of whom 236 agreed to participate in the study procedures. After screening and informed consent, 170 participants were enrolled and completed at least part of the study protocol (64–91 years; mean age: 73 ± 7 years).

Data availability varied across assessments, and the number of valid observations for each variable and analysis is reported in the corresponding tables and Results sections. Missing PWVc-f values were mainly attributable to technically unacceptable waveforms or incomplete vascular assessments. No systematic pattern of missingness was identified.

The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the Faculty of Sport at the University of Porto (CEFADE 06.2016). All participants provided written informed consent before participation.

2.2. Study Design

The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [23]. Participant recruitment and data collection were conducted between October 2016 and October 2017 at the research center facilities.

Participants attended two laboratory visits separated by seven days. During the first visit, participants completed medical history, anthropometric, and functional fitness assessments, followed by 7 days of accelerometer monitoring of habitual physical activity. After seven consecutive days of monitoring, participants returned to the laboratory for accelerometer retrieval and cardiovascular assessments, including blood pressure and carotid–femoral pulse wave velocity (PWVc-f) measurements.

2.3. Medical History and Anthropometric Assessments

Clinical and lifestyle information, including medication use, smoking habits, alcohol consumption, hypertension, diabetes mellitus, cardiovascular disease, kidney disease, and endocrine disorders, was obtained through a semi-structured interview.

Body mass and height were measured using a calibrated digital scale and stadiometer (Tanita™ Scan BC532, Tokyo, Japan), and body mass index (BMI) was calculated as body mass divided by height squared (kg/m²). Participants were categorized as normal weight (<25.0 kg/m²), overweight (25.0–29.9 kg/m²), or obesity (≥30.0 kg/m²). For selected analyses, overweight and obesity categories were combined.

Waist circumference was measured at the midpoint between the lower rib margin and the iliac crest, whereas hip circumference was obtained at the largest circumference of the buttocks. Waist-to-hip ratio (WHR) was subsequently calculated. Elevated cardiovascular risk was defined as WHR > 1.03 (males) and >0.90 (females) according to ACSM guidelines [21].

2.4. Functional Fitness Assessment

Functional fitness was assessed using the Senior Fitness Test battery (formerly Fullerton Functional Fitness Test), which evaluates multiple physical domains associated with functional independence in older adults [24,25]. The assessment included the 30-s chair stand, arm curl, chair sit-and-reach, back scratch, 8-foot up-and-go, and 6-min walk tests. All tests were conducted in the morning following a standardized 10-min warm-up by trained evaluators. Previously published age- and sex-specific normative values were used as reference criteria.

An overall functional fitness score (0–6 points) was calculated by assigning one point for each test in which performance met or exceeded the corresponding normative reference value. This approach was adopted to provide a multidimensional estimate of overall functional capacity across mobility, muscular fitness, flexibility, balance/agility, and aerobic endurance domains.

For categorical analyses, high functional fitness was operationally defined as meeting or exceeding the age- and sex-specific normative value in at least five of the six tests. Participants meeting normative values in four or fewer tests were classified as having low functional fitness. Because this operational threshold has not been externally validated, the continuous composite score was retained for the principal correlation and linear regression analyses.

2.5. Physical Activity and Sedentary Behavior

Habitual physical activity and sedentary behavior were objectively assessed using uniaxial accelerometers (ActiGraph™ GT1M; ActiGraph LLC, Pensacola, FL, USA). Participants were instructed to wear the device on an elastic belt positioned over the right hip during waking hours for seven consecutive days, removing it only for bathing, swimming, or other water-based activities. Raw acceleration data were sampled at 30 Hz and processed using ActiLife™ software (version 6.11.4; ActiGraph LLC), with activity counts aggregated into 60-s epochs. Accelerometer data were processed according to the standardized procedures described by Troiano et al. [26]. Non-wear time was defined as at least 60 consecutive minutes of zero counts, allowing interruptions of up to 2 min with counts between 0 and 100 counts·min⁻¹. A valid monitoring day required at least 10 h of wear time, and participants were included in accelerometer-based analyses if they provided at least three valid days, including two weekdays and one weekend day [26]. Participants who did not satisfy these wear-time criteria were excluded from accelerometer-derived analyses.

Physical activity intensity was classified according to established count thresholds [26]: sedentary behavior (0–99 counts/min), light-intensity physical activity (100–2019 counts/min), and moderate-to-vigorous physical activity (MVPA; ≥2020 counts/min). Although alternative thresholds have been proposed for older adults, the present study adopted the standardized ActiLife/Troiano cut-points because they remain among the most widely used in epidemiological research and facilitate comparison with previous studies evaluating adherence to current physical activity recommendations.

Weekly MVPA was estimated by multiplying the average daily MVPA accumulated across valid monitoring days by seven. Participants accumulating at least 150 min·week⁻¹ of MVPA were classified as meeting current physical activity recommendations, whereas those accumulating <150 min·week⁻¹ were classified as insufficiently active [21]. For descriptive purposes, sedentary time was additionally categorized according to the sample distribution, with participants in the highest quartile (≥75th percentile) classified as highly sedentary.

2.6. Blood Pressure and Arterial Stiffness Assessments

Participants were instructed to refrain from food intake, caffeine, alcohol consumption, smoking, and vigorous physical activity for at least 60 min before cardiovascular assessments. After 10 min of seated rest in a quiet temperature-controlled room, brachial blood pressure was measured three times at 1-min intervals using a semi-automated oscillometric device (Colin BP-8800, Critikron™, Tampa, FL, USA). The average of the three measurements was used for analysis. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication.

Central arterial stiffness was assessed in the supine position by carotid–femoral pulse wave velocity (PWVc-f) using applanation tonometry (SphygmoCor™ version 8.0, AtCor Medical, Sydney, Australia), according to expert consensus recommendations and established reference values [27,28,29]. Central aortic waveforms were derived from radial artery pulse wave analysis using a validated generalized transfer function applied to the average of 10 consecutive radial waveforms. Carotid and femoral pulse waveforms were obtained from the non-dominant side using a Millar tonometer (Millar™ Instruments, Houston, TX, USA), with simultaneous electrocardiographic gating.

Pulse transit time was calculated as the mean time difference between the carotid and femoral waveforms relative to the ECG over a 10-s recording period. Pulse transit distance was measured as the surface distance from the femoral pulse recording site to the sternal notch via the umbilicus minus the distance from the carotid pulse recording site to the sternal notch, in accordance with the Reference Values for Arterial Stiffness Collaboration [30]. PWVc-f was calculated as the ratio between pulse transit distance and pulse transit time [30].

Quality control procedures included device calibration before testing, duplicate PWVc-f measurements, acquisition of a third measurement whenever consecutive values differed by >0.5 m/s, and adherence to the manufacturer’s operating procedures and international consensus recommendations [27,28,29]. Central systolic blood pressure was estimated from the radial waveforms using the validated generalized transfer function [28,29]. Elevated arterial stiffness was defined as PWVc-f > 10 m/s [27,30].

Cardiovascular assessments were performed under standardized pre-assessment conditions but were not scheduled at a fixed time of day for all participants.

2.7. Statistical Analysis

Data normality was assessed using the Kolmogorov–Smirnov test and visual inspection of distribution plots. Continuous variables are presented as mean ± standard deviation (SD), whereas categorical variables are expressed as absolute and relative frequencies. Sex differences were explored using independent-samples Student’s t tests, Mann–Whitney U tests, or chi-square tests, as appropriate. Because the original sample size calculation was based on the overall study sample rather than sex-specific comparisons, these analyses were considered exploratory and were interpreted with particular caution given the smaller male subgroup (n = 46).

Spearman correlation analyses were performed to examine the associations between PWVc-f and individual functional fitness components, as well as the composite functional fitness score. Because some functional fitness variables were unavailable for all participants, Spearman correlations were performed using pairwise deletion to maximize the number of observations available for each variable pair. Consequently, the analytic sample varied across correlations according to data availability.

Independent correlates of arterial stiffness were investigated using hierarchical multiple linear regression, with PWVc-f entered as the dependent variable. Predictors were entered sequentially according to their established relevance as determinants of arterial stiffness. Model 1 included age and sex; Model 2 additionally incorporated BMI and hypertension status; Model 3 further included MVPA and sedentary time; and Model 4 added the composite functional fitness score. Changes in explained variance (ΔR²) were calculated at each step to determine the incremental contribution of each block of variables. Regression results are presented as unstandardized regression coefficients (B), standard errors (SEs), standardized coefficients (β), 95% confidence intervals (95% CIs), and p-values. A supplementary Spearman correlation matrix for the variables included in the multivariable analyses is provided in Supplementary Table S1. Exploratory interaction terms (sex × functional fitness score and sex × MVPA) were entered separately into the final hierarchical regression model to examine whether sex modified the associations of functional fitness and MVPA with PWVc-f.

Binary logistic regression analyses were subsequently performed to estimate odds ratios (ORs) and 95% confidence intervals (95% CIi) for elevated arterial stiffness (PWVc-f > 10 m/s). Separate crude and adjusted models were constructed for functional fitness classification (high vs. low) and compliance with current MVPA recommendations (≥150 vs. <150 min/week). Adjusted models included age, sex, BMI, and hypertension status as covariates.

To further examine whether the association between age and arterial stiffness differed according to functional fitness, an exploratory two-way analysis of variance (ANOVA) was performed using predefined age strata and functional fitness classification (high vs. low). Main effects for age group and functional fitness, as well as their interaction, were examined.

Model assumptions were evaluated through residual analyses. Multicollinearity in the regression models was assessed using variance inflation factors (VIFs) and tolerance statistics, with VIF values < 5 and tolerance values > 0.20 considered indicative of acceptable collinearity. Missing values were not imputed. Analyses were conducted using available-case procedures appropriate for each statistical method. Spearman correlation analyses used pairwise deletion, whereas regression models and the exploratory ANOVA were conducted using complete-case analysis. The analytic sample size is reported for each analysis.

Statistical significance was set at p ≤ 0.05. All statistical analyses were performed using SPSS version 29.0 (IBM Corp., Armonk, NY, USA).

3. Results

3.1. Participant Characteristics

Table 1. Participant characteristics.

Variable	Total Sample (n = 170)	Females (n = 124)	Males (n = 46)	p-Value
Demographic and anthropometric characteristics
Age (years)	72.3 ± 6.7	72.0 ± 6.6	73.3 ± 7.1	0.263
Body mass (kg)	71.4 ± 12.5	68.3 ± 10.8	80.0 ± 12.6	<0.001
BMI (kg/m2)	29.4 ± 4.5	29.3 ± 4.3	29.5 ± 5.0	0.760
Normal weight, n (%)	29 (17.1)	21 (16.9)	8 (17.4)	0.866 †
Overweight, n (%)	72 (42.4)	54 (43.5)	18 (39.1)
Obesity, n (%)	69 (40.6)	49 (39.5)	20 (43.5)
Waist-to-hip ratio	0.91 ± 0.07	0.89 ± 0.07	0.97 ± 0.05	<0.001
Clinical characteristics
Hypertension, n (%)	85/165 (51.5)	62/121 (51.2)	23/44 (52.3)	1.000
Diabetes mellitus, n (%)	37/165 (22.4)	29/121 (24.0)	8/44 (18.2)	0.564
Current smoking, n (%)	23/159 (14.5)	10/118 (8.5)	13/41 (31.7)	<0.001
Antihypertensive medication, n (%)	62/170 (36.5)	37/124 (29.8%)	25/46 (54.3%)	0.013
Lipid-lowering medication (statins), n (%)	47/170 (27.6%)	32/124 (25.8%)	15/46 (32.6%)	0.491
Hemodynamic and vascular characteristics
Resting heart rate (bpm)	68.2 ± 10.8	69.5 ± 10.5	64.8 ± 10.8	0.013
SBP (mmHg)	132.3 ± 17.5	132.2 ± 18.2	132.6 ± 15.5	0.895
DBP (mmHg)	70.6 ± 9.1	70.0 ± 8.7	72.3 ± 9.8	0.159
PWVc-f (m/s) (n = 154)	11.2 ± 2.7	10.9 ± 2.6	12.1 ± 2.9	0.013
PWVc-f > 10 m/s, n (%)	96/154 (62.3)	64/112 (57.1)	32/42 (76.2)	0.047
MVPA (min/day) (n = 134)	20.5 ± 19.2	19.4 ± 18.8	23.7 ± 20.2	0.251
Physical activity and sedentary behavior
Sedentary time (min/day) (n = 134)	449.9 ± 83.6	440.6 ± 86.5	477.1 ± 68.6	0.027
Functional fitness score (0–6 points) (n = 161)	4.1 ± 1.4	3.9 ± 1.4	4.4 ± 1.4	0.032
Low functional fitness, n (%)	90/161 (55.9)	72/117 (61.5)	18/44 (40.9)	0.030

Values are presented as mean ± SD or n (%). ^† A single p-value refers to the overall comparison across the three BMI categories using Pearson’s chi-square test. Sample size varied across variables because of missing data.

summarizes the demographic, anthropometric, clinical, hemodynamic, vascular, physical activity, and functional fitness characteristics of the study sample, overall and stratified by sex. The sample was predominantly composed of women (72.9%) and had a mean age of 72.3 ± 6.7 years, with no significant age difference between women and men. Excess body weight was highly prevalent, with 42.4% and 40.6% of participants classified as overweight and obese, respectively, whereas only 17.1% had normal weight. Mean BMI was 29.4 ± 4.5 kg/m² and did not differ between sexes, although men had greater body mass and waist-to-hip ratio (both p < 0.001). Hypertension, diabetes mellitus, current smoking, and antihypertensive medication use were reported by 51.5%, 22.4%, 14.5%, and 36.5% of participants, respectively, whereas 27.6% were receiving lipid-lowering medication. Compared with women, men were more frequently current smokers (31.7% vs. 8.5%, p < 0.001) and users of antihypertensive medication (54.3% vs. 29.8%, p = 0.013), whereas the prevalence of hypertension, diabetes mellitus, obesity, and lipid-lowering medication use did not differ significantly between sexes.

Mean systolic blood pressure was 132.3 ± 17.5 mmHg and mean PWVc-f was 11.2 ± 2.7 m/s, with 62.3% of participants exceeding the clinical threshold for elevated arterial stiffness (>10 m/s). Men exhibited higher PWVc-f values and a greater prevalence of elevated arterial stiffness than women (both p < 0.05), despite similar brachial blood pressure levels. Daily MVPA averaged 20.5 ± 19.2 min and did not differ significantly between sexes, whereas men accumulated more sedentary time (477.1 ± 68.6 vs. 440.6 ± 86.5 min/day, p = 0.027). Functional fitness was generally low according to age-specific normative values, with a mean composite functional fitness score of 4.1 ± 1.4 points and 55.9% of participants classified as having low functional fitness. Nevertheless, men presented higher functional fitness scores and a lower prevalence of low functional fitness than women (both p < 0.05).

3.2. Exploratory Comparisons by Sex

Table 2. Sex differences in hemodynamic, vascular, physical activity, and functional fitness variables.

Variable	Females (n = 124)	Males (n = 46)	p-Value
Hemodynamic and vascular characteristics
Resting heart rate (bpm)	69.9 ± 11.1	64.9 ± 10.1	0.011
SBP (mmHg)	132.6 ± 18.4	131.6 ± 15.0	0.740
DBP (mmHg)	70.2 ± 9.0	71.8 ± 9.4	0.334
Pulse pressure (mmHg)	62.4 ± 13.8	59.8 ± 12.2	0.277
Aortic SBP (mmHg)	124.5 ± 17.1	125.5 ± 16.3	0.767
Aortic pulse pressure (mmHg)	53.3 ± 13.6	52.8 ± 11.0	0.834
PWVc-f (m/s)	10.9 ± 2.6	12.1 ± 2.9	0.010
PWVc-f > 10 m/s, n (%)	64/112 (57.1)	32/42 (76.2)	0.030
Physical activity and sedentary behavior
MVPA (min/day)	19 ± 19	24 ± 20	0.251
Sedentary time (min/day)	441 ± 87	477 ± 69	0.027
MVPA < 150 min/week, n (%)	71/100 (71.0)	17/33 (51.5)	0.040
Functional fitness components
Chair stand (repetitions)	16 ± 5	18 ± 7	0.039
Arm curl (repetitions)	18 ± 5	19 ± 9	0.889
Chair sit-and-reach (cm)	−8.6 ± 12.8	−8.9 ± 16.0	0.722
Back scratch (cm)	−15.5 ± 13.7	−17.9 ± 15.6	0.322
8-foot up-and-go (s)	6.6 ± 3.6	5.6 ± 2.8	0.790
6-min walk (m)	497.5 ± 139.3	556.1 ± 125.7	0.035
Overall functional fitness
Functional fitness score (0–6 points)	3.91 ± 1.36	4.43 ± 1.35	0.032
Low functional fitness, n (%)	72/117 (61.5)	18/44 (40.9)	0.019

Values are presented as mean ± SD or n (%). PWVc-f: carotid–femoral pulse wave velocity; MVPA: moderate-to-vigorous physical activity; SBP: systolic blood pressure; DBP: diastolic blood pressure.

presents exploratory sex-specific comparisons of hemodynamic, vascular, physical activity, and functional fitness variables. Given the smaller number of male participants and the fact that the study was not specifically powered for sex-stratified analyses, these comparisons should be interpreted cautiously.

Men exhibited lower resting heart rate (p = 0.011) and higher PWVc-f values than women (12.1 ± 2.9 vs. 10.9 ± 2.6 m/s, p = 0.010), whereas peripheral and central blood pressure measures did not differ between sexes. Consistent with these findings, the prevalence of elevated arterial stiffness (PWVc-f > 10 m/s) was greater among men than women (76.2% vs. 57.1%, p = 0.030). Sex differences in movement behaviors were modest. Mean MVPA was similar between groups, although women accumulated less sedentary time than men (441 ± 87 vs. 477 ± 69 min/day, p = 0.027). Conversely, the prevalence of insufficient MVPA (<150 min/week) was higher among women (71.0% vs. 51.5%, p = 0.040).

Men performed better in the chair stand and 6-min walk tests (p < 0.05), whereas no sex differences were observed for upper-body strength, flexibility, or agility. They also achieved higher composite functional fitness scores than women (4.43 ± 1.35 vs. 3.91 ± 1.36 points, p = 0.032). Accordingly, low functional fitness was more prevalent among women than men (61.5% vs. 40.9%, p = 0.019).

3.3. Associations Between Arterial Stiffness and Functional Fitness

Spearman correlation analyses demonstrated significant associations between PWVc-f and all functional fitness measures (

Table 3. Spearman correlations between PWVc-f and functional fitness variables.

Variable	Spearman Rho	p-Value	n
Chair stand (repetitions)	−0.366	<0.001	147
Arm curl (repetitions)	−0.445	<0.001	145
Chair sit-and-reach (cm)	−0.211	0.010	148
Back scratch (cm)	−0.261	0.001	148
8-foot up-and-go (s)	0.479	<0.001	146
6-min walk (m)	−0.428	<0.001	103
Functional fitness score (0–6 points)	−0.338	<0.001	148

PWVc-f: carotid–femoral pulse wave velocity. Spearman’s rank correlation coefficient (rho). n varies across correlations because pairwise deletion was used to handle missing data.

). Higher PWVc-f values were consistently associated with poorer functional performance across all tests, with the strongest association observed for the 8-foot up-and-go test (ρ = 0.479, p < 0.001), followed by the arm curl (ρ = −0.445), 6-min walk (ρ = −0.428), chair stand (ρ = −0.366), the composite functional fitness score (ρ = −0.338), back scratch (ρ = −0.261), and chair sit-and-reach (ρ = −0.211), with all correlations reaching statistical significance (all p ≤ 0.010). Correlations among demographic, clinical, hemodynamic, movement behavior, functional fitness, and arterial stiffness variables are presented in Supplementary Table S1.

3.4. Independent Correlates of Arterial Stiffness

Table 4. Hierarchical multiple linear regression models examining independent correlates of carotid–femoral pulse wave velocity.

Model	Predictor	n	R2	Adjusted R2	ΔR2	p for ΔR2
1	Age, Sex	121	0.404	0.394	0.404	<0.001
2	+BMI, Hypertension	121	0.415	0.399	0.011	0.342
3	+MVPA, Sedentary time	121	0.415	0.391	<0.001	0.996
4	+Functional fitness score	121	0.418	0.390	0.003	0.455
Predictor (final model)	B (SE)	β	95% CI			p-value
Age	0.238 (0.038)	0.570	0.162 to 0.313			<0.001
Sex	0.857 (0.476)	0.135	−0.086 to 1.799			0.074
BMI	−0.082 (0.052)	−0.124	−0.185 to 0.021			0.117
Hypertension	0.202 (0.431)	0.036	−0.652 to 1.055			0.641
MVPA	0.001 (0.011)	0.005	−0.022 to 0.023			0.952
Sedentary time	0.0003 (0.003)	0.008	−0.005 to 0.005			0.914
Functional fitness score	−0.137 (0.183)	−0.066	−0.499 to 0.225			0.455

PWVc-f: carotid–femoral pulse wave velocity; BMI: body mass index; MVPA: moderate-to-vigorous physical activity; B: unstandardized regression coefficient; SE: standard error; β: standardized regression coefficient; 95% CI: 95% confidence interval. Age, BMI, MVPA, sedentary time, and functional fitness score were entered as continuous variables. Sex (female = 0, male = 1) and hypertension (no = 0, yes = 1) were entered as dichotomous variables. n = 121 for all models because hierarchical regression analyses were performed using complete-case (listwise) analysis of missing values.

presents the hierarchical multiple regression models examining independent correlates of PWVc-f, including unstandardized regression coefficients (B), standard errors (SEs), standardized coefficients (β), 95% confidence intervals (95% CIs), and p-values for the final model. Age and sex (Model 1) explained 40.4% of the variance in PWVc-f (R² = 0.404, p < 0.001). The addition of BMI and hypertension status (Model 2) resulted in only a small and non-significant increase in explained variance (ΔR² = 0.011, p = 0.342). Neither MVPA nor sedentary time (Model 3) improved model fit (ΔR² < 0.001, p = 0.996). Similarly, adding the composite functional fitness score in Model 4 resulted in only a trivial, non-significant increase in explained variance (ΔR² = 0.003, p = 0.455). Multicollinearity was low across all models, with VIF values ranging from 1.10 to 1.63 and tolerance values exceeding 0.60 for all predictors. Correlations among the independent variables were generally low to moderate (Supplementary Table S1), consistent with these findings.

In the final model, only age remained independently associated with PWVc-f (β = 0.570, p < 0.001), whereas sex showed a borderline association (β = 0.135, p = 0.074). BMI, hypertension status, MVPA, sedentary time, and the composite functional fitness score were not independently associated with arterial stiffness. Additional exploratory hierarchical regression models including the interaction term between sex and functional fitness (p = 0.846) or between sex and MVPA (p = 0.693) showed no evidence of effect modification by sex. However, given the limited number of male participants, interaction analyses may have had insufficient power to detect small or moderate sex-specific effects. Given the consistent attenuation of the associations between functional fitness and PWVc-f after multivariable adjustment, an exploratory analysis was subsequently performed to examine whether the relationship between age and arterial stiffness differed according to functional fitness level.

3.5. Functional Fitness, Physical Activity, and Elevated Arterial Stiffness

Logistic regression analyses examining factors associated with elevated arterial stiffness (PWVc-f > 10 m/s) are presented in

Table 5. Crude and adjusted logistic regression models for elevated arterial stiffness (PWVc-f >10 m/s).

Predictor	n	Crude OR (95% CI)	p-Value	Adjusted OR (95% CI) †	p-Value
High functional fitness	148	0.39 (0.20 to 0.78)	0.007	0.56 (0.25 to 1.29)	0.176
MVPA ≥150 min/week	134	0.68 (0.32 to 1.45)	0.319	1.21 (0.48 to 3.06)	0.690

^† Adjusted for age, sex, BMI, and hypertension. PWVc-f, carotid–femoral pulse wave velocity; MVPA, moderate-to-vigorous physical activity; BMI, body mass index. High functional fitness and MVPA ≥150 min/week were coded as exposure categories. Elevated arterial stiffness was defined as PWVc-f >10 m/s. Analytic sample sizes differed because separate logistic regression models were fitted for each exposure using complete-case (listwise) analysis.

. In the unadjusted model, participants with high functional fitness exhibited significantly lower odds of elevated arterial stiffness than those with low functional fitness (OR = 0.39, 95% CI = 0.20–0.78, p = 0.007). However, this association was attenuated and no longer statistically significant after adjustment for age, sex, BMI, and hypertension status (OR = 0.56, 95% CI = 0.25–1.29, p = 0.176). Meeting the recommendation of at least 150 min/week of MVPA was not associated with elevated arterial stiffness in either the crude (OR = 0.68, 95% CI = 0.32–1.45, p = 0.319) or adjusted models (OR = 1.21, 95% CI = 0.48–3.06, p = 0.690).

3.6. Exploratory Analysis

Results of the exploratory two-way ANOVA indicated significant differences in PWVc-f across age groups (F = 16.985, p < 0.001, partial η² = 0.267). In contrast, neither the main effect of functional fitness classification (F = 0.789, p = 0.376, partial η² = 0.006) nor the interaction between age group and functional fitness (F = 0.358, p = 0.784, partial η² = 0.008) reached statistical significance. These findings provide no evidence that the age-related pattern of arterial stiffness differs according to functional fitness classification.

4. Discussion

This study examined the associations of objectively measured physical activity, sedentary behavior, and multidimensional functional fitness with PWVc-f in community-dwelling older adults. The main findings were: (a) arterial stiffness was inversely associated with all dimensions of functional fitness, whereas no meaningful associations were observed for objectively measured physical activity or sedentary behavior, and (b) participants with higher functional fitness exhibited substantially lower odds of elevated arterial stiffness in unadjusted analyses; (c) however, these associations were markedly attenuated after adjustment for age and other established demographic and clinical correlates, with age emerging as the only consistent independent correlate of PWVc-f across all regression models.

The prominent influence of age presently observed is consistent with current understanding of vascular aging. Central arterial stiffness increases progressively throughout life as a consequence of cumulative structural and functional alterations of the arterial wall, including elastin fragmentation, collagen deposition, medial calcification, endothelial dysfunction, and chronic low-grade inflammation [8,27,29]. Accordingly, age has consistently been identified as the strongest determinant of carotid–femoral PWV across different populations, often outweighing the contribution of modifiable lifestyle factors [1,3,6].

Our findings should therefore be interpreted within this broader context of vascular aging. Although functional fitness demonstrated stronger unadjusted associations with PWVc-f than objectively measured physical activity or sedentary behavior, these relationships were no longer statistically significant after accounting for age and other established correlates. Exploratory analyses likewise provided no evidence that the age-related pattern of arterial stiffness differed according to functional fitness level. Together, these findings indicate that the observed association between functional fitness and arterial stiffness is largely explained by age-related physiological processes rather than by an independent relationship between functional fitness and vascular stiffness. Although sex-related differences may influence trajectories of functional and vascular aging, exploratory analyses did not identify evidence of effect modification by sex in the present sample.

One possible biological explanation for these findings is that functional fitness integrates the performance of multiple physiological systems that progressively decline with advancing age [21]. These same age-related processes also contribute to arterial stiffening through structural and functional alterations of the arterial wall [8,29], providing a common physiological basis for the observed associations.

Previous studies likewise support this interpretation. Lower carotid–femoral PWV has been associated with greater cardiorespiratory fitness [31], muscular strength [19], gait performance and physical function [32], as well as with composite measures of physical performance [33,34,35,36]. Most previous investigations, however, have evaluated isolated fitness components rather than multidimensional functional capacity, and relatively few have examined these associations after simultaneously accounting for major determinants of vascular aging. Our results therefore extend the existing literature by demonstrating that multidimensional functional fitness exhibits stronger unadjusted associations with central arterial stiffness than objectively measured movement behaviors, while showing that these associations are largely attributable to age.

From a clinical perspective, these findings suggest that functional fitness should be interpreted primarily as an integrated indicator of age-related physiological decline rather than as an independent determinant of arterial stiffness. Functional performance reflects the cumulative effects of aging across multiple physiological systems, many of which are also implicated in vascular aging. Accordingly, its association with PWVc-f is better viewed as an expression of the close interplay between functional and vascular aging than as evidence of a direct vascular effect.

Nevertheless, the cross-sectional nature of the present study does not preclude a beneficial effect of improving functional fitness on vascular health. Exercise interventions capable of enhancing functional fitness have been shown to improve endothelial function, autonomic regulation, blood pressure control, vascular remodeling, and systemic inflammation [10,37]. Accordingly, the absence of an independent association should be interpreted with caution, as observational analyses cannot distinguish shared age-related processes from causal relationships. Whether maintaining or improving functional fitness can attenuate the progression of arterial stiffening remains an important question to be addressed in longitudinal studies and randomized controlled trials.

Contrary to our initial hypothesis, objectively measured physical activity and sedentary behavior were not associated with PWVc-f in the present study. Neither MVPA nor sedentary time correlated significantly with arterial stiffness or independently predicted elevated PWVc-f in the adjusted analyses. Although these results appear to contrast with the well-established cardiovascular benefits of regular physical activity [10,12,13,18,32], they are consistent with systematic reviews indicating that associations between objectively measured habitual physical activity and central arterial stiffness are generally modest [13,14].

One explanation relates to the method used to quantify habitual physical activity. Systematic reviews have consistently shown that studies relying on self-reported questionnaires tend to report stronger associations with arterial stiffness than those using accelerometry [13,14]. Whereas questionnaires are susceptible to recall bias and misclassification of activity intensity and duration, accelerometers provide a more objective estimate of habitual movement behavior. Consequently, objectively measured physical activity may demonstrate weaker associations with PWVc-f despite providing greater measurement validity.

A second explanation is that habitual movement behavior represents only one of several determinants of vascular aging. Arterial stiffness reflects the cumulative influence of aging, blood pressure, adiposity, metabolic health, inflammation, and other long-term physiological processes [8,29]. Within this multifactorial framework, the relatively small contribution of daily movement behavior observed in the present study appears biologically plausible.

Finally, characteristics of the study sample may also have influenced these findings. Participants accumulated relatively little MVPA and spent prolonged periods in sedentary behavior, resulting in a restricted range of movement behaviors. Such limited variability may reduce the ability to detect statistically significant associations, particularly when expected effect sizes are small. Hence, objectively measured movement behaviors alone may not adequately capture the complex physiological processes underlying vascular aging in community-dwelling older adults.

Our findings have practical implications. Although functional fitness was not independently associated with arterial stiffness after adjustment for age and other established correlates, it consistently exhibited stronger unadjusted associations with PWVc-f than objectively measured physical activity or sedentary behavior. These observations reinforce current recommendations advocating the routine assessment of physical function in older adults [21]. Simple performance-based tests may provide clinically useful information about overall physiological status and help identify individuals with less favorable vascular profiles, even if they do not represent independent determinants of arterial stiffness.

The interpretation of these findings is strengthened by several methodological features. Physical activity and sedentary behavior were objectively assessed using accelerometry, minimizing recall bias and improving measurement precision. Arterial stiffness was evaluated by carotid–femoral PWV, the reference standard for assessing central arterial stiffness [8,29]. In addition, functional fitness was assessed using the Fullerton Functional Fitness Test, allowing the construction of a multidimensional composite score that better reflects overall physical function than isolated fitness measures.

Several limitations should also be acknowledged. The cross-sectional design precludes causal inference. Therefore, it cannot be determined whether reduced functional fitness contributes to arterial stiffening, whether vascular aging contributes to functional decline, or whether both processes evolve concurrently with advancing age. Although major confounders, including age, sex, BMI, and hypertension status, were included in the adjusted analyses, residual confounding by unmeasured variables cannot be excluded. In addition, the study sample was predominantly composed of women.

Although exploratory interaction analyses provided no evidence that the associations between functional fitness, movement behaviors, and arterial stiffness differed according to sex, the study was not specifically powered for sex-stratified analyses. The a priori power calculation referred to the overall sample, and the relatively small male subgroup (n = 46) may have limited the precision of sex-specific estimates and the ability to detect subtle between-sex differences or interactions. Therefore, whether the strength of the observed associations between functional fitness and PWVc-f is similar in older women and men remains an important question for future research. Consequently, the generalizability of these findings to older men should be interpreted with caution, and future studies including more balanced sex distributions are warranted to confirm whether these relationships differ between women and men.

The relatively low levels of MVPA and high prevalence of sedentary behavior may also have limited the ability to detect associations between objectively measured movement behaviors and PWVc-f. Finally, because no validated thresholds currently exist for the composite functional fitness score, participants were classified using a sample-derived cut-off, which may limit the external applicability of this classification.

5. Conclusions

Multidimensional functional fitness demonstrated stronger unadjusted associations with central arterial stiffness than objectively measured physical activity or sedentary behavior in community-dwelling older adults. However, these associations were largely explained by age, which emerged as the only consistent independent correlate of carotid–femoral pulse wave velocity after multivariable adjustment. These findings indicate that the observed association between functional fitness and arterial stiffness is largely accounted for by age and highlight the importance of considering age when interpreting the relationship between physical function and vascular health in older adults. Longitudinal studies and randomized controlled trials are needed to determine whether interventions that improve functional fitness also influence the progression of arterial stiffness beyond that expected with chronological aging.