Assessment of Frailty Among Older Adults in the Physical Activity Daily Trial

Abstract

Frailty is common among cardiac patients; however, frailty assessment data from patients with peripheral arterial disease (PAD) are limited. The purpose of this observational study was to identify the prevalence and factors related to frailty in addition to unique frailty marker groupings in a cohort of sedentary adults with PAD. We grouped three PAD-relevant frailty characteristics using Fried’s frailty phenotype −1) exhaustion, (2) weakness, and (3) slowness—and observed the prevalence of pre-frailty (1–2 characteristics) and frailty (3 characteristics) in the PAD cohort. Of the 106 participants, 34.9% were robust/non-frail, 53.8% were pre-frail, and 2.8% were frail. Exhaustion (33.3%) was the most occurring characteristic followed by weakness (20.0%) and slowness (5.0%). The grouping of weakness + slowness (10.0%) was the most prevalent followed by exhaustion + weakness (8.3%) and exhaustion + slowness (5.0%). Among pre-frail participants, ankle brachial index was correlated with a reduction in gait speed.

Keywords

peripheral artery disease markers phenotype

What this paper adds

• Unique groupings of frailty characteristics are identified in patients with PAD.

• The presence of PAD was correlated with a slower gait speed among participants in this cohort.

Applications of study findings

• The grouping of frailty characteristics in patients with PAD is imperative to develop and implement prevention strategies to delay the progression of pre-frailty to frailty into clinical practice.

• Future studies considering frailty among PAD may consider looking at how the components of frailty are clustered.

Introduction

An estimated 8.5 million Americans over the age of 40 carry a diagnosis of peripheral artery disease (PAD) with a greater prevalence occurring among older individuals (Benjamin et al., 2019; Hirsch et al., 2001). Diagnosis of PAD is determined by the ankle brachial index (ABI), a noninvasive measure of atherosclerosis, with a diagnostic value of ≤0.9. In a large meta-analysis of seven community-based cohorts, prevalence rates for PAD were observed up to 24.7% in females and 29.4% in males 80 years of age or older (Allison et al., 2007). With an aging global population, it is not surprising that the prevalence of PAD has increased by more than 15% over the last decade. Currently, an estimated 200 million adults worldwide have PAD (Criqui & Aboyans, 2015; Fowkes et al., 2013).

Adults with PAD have a 60% higher risk for all-cause mortality and a 96% increased risk for cardiovascular (CV) mortality compared to those without PAD, after adjustment of age and traditional CV risk factors (Fowkes et al., 2008; Heald et al., 2006). Markers of frailty, including slow gait speed, low grip strength, and exhaustion, have been associated with worse outcomes in other CV conditions such as heart disease (Afilalo J., 2011; Veronese et al., 2017b). However, less is known regarding markers of frailty in adults with PAD. Lower ABI values (<0.5), indicative of severe PAD, is associated with malnutrition and impaired physical function, such as grip strength and gait speed, as a result of decreased perfusion but the correlation of ABI values to frailty status is less unknown (Martin-Ponce et al., 2014; Matsushita et al., 2017; Tanaka et al., 2016). Additionally, the extent to which frailty may be correlated with measures of PAD severity is less known. Therefore, we sought to examine demographic and clinical characteristics associated with markers of frailty (gait speed, grip strength, and self-reported exhaustion) from a cohort of adults with PAD, and to determine the prevalence and groupings of these metrics using baseline data from a clinical cohort of adults with PAD. Secondary analyses were conducted to examine the relationship of ABI values with markers of frailty and frailty status within this cohort. We hypothesized that the prevalence of frailty and pre-frailty would differ within this cohort, with specific frailty characteristics occurring more frequently.

Methods

The Physical Activity Daily Trial (IRB-300001012) is a randomized controlled trial examining the feasibility and efficacy of an internet-based walking program compared with a telephone counseling intervention, a combination of both internet walking program with the telephone counseling or usual with the objective to promote regular walking in patients with PAD (Kumar et al., 2018). Participants were recruited through PAD specialty clinics at two academic institutions in the United States. Outcomes were assessed at baseline, 4 months, and 12 months. Participants provided informed consent prior to the administration of any component of the study all components of the study were in accordance with the Helsinki Declaration of 1975 as revised in 2000. Here, we present baseline data from the Physical Activity Daily Trial and assess frailty occurrence.

Participants were eligible for the study if they were 40 years of age or older with PAD, documented by one of the following: (1) ankle brachial index (ABI) ≤ 0.9 in at least one leg; (2) toe brachial index (TBI) ≤ 0.7 for participants with ABI > 1.3; or (3) imaging through peripheral angiograms for participants with ABI 0.91 to 1.3 (Hirsch et al., 2006). Adults who reported participating in regular exercise programs or performed >150 minutes per week of physical activity were excluded. Notably important to the present study, participants were also excluded for unstable cardiac conditions or recent cardiovascular event (e.g., myocardial infarction or stroke) within 3 months of enrollment nor could they have any planned revascularization procedures. Lastly, participants had to have access to email and the internet. Additional inclusion and exclusion criteria are reported elsewhere (Kumar et al., 2018).

Demographic characteristics including age, sex, and race/ethnicity were collected at baseline. Participants also self-reported marital status, educational level and employment history at baseline. Standard questionnaires were used to collect information on medical conditions and smoking history. BMI was calculated using weight and height from the baseline visit. For participants missing baseline demographics, height, or weight we input the available data from the closest trial assessment visit (e.g., for participants missing height (two) at baseline, we collected height values from visit at 4 months). Additional functional measures of gait speed, grip strength, and exhaustion were assessed at baseline.

ABIs were measured at baseline in each participant using a standard, validated protocol (Hirsch et al., 2006). ABI was measured following a 10-min quiet rest in the supine position. Systolic blood pressure in the dorsalis pedis and posterior tibial arteries were assessed in both ankles and brachial pressure was measured in both arms using Doppler ultrasonography (Ultrasonic Doppler Flow Detector Model 811-B, Parks Medical Electronics, Inc., Aloha, OR). The ABI in each lower extremity was calculated by dividing the ankle reading by the higher of the brachial readings. The average between left and right legs were further computed.

As per the Fried criteria for frailty (Fried et al., 2001), the functional assessments of gait speed, hand grip, and self-reported exhaustion were measured at baseline. Gait speed was assessed using a standard 5-m timed walk. Participants were instructed to walk their typical pace from the starting line (0 m) through the finish line (6 m). The timer began at 0.5 m and stopped at 5.5 m, totaling 5 m walking distance. The participants performed three trials with a minimum 15-s rest. The fastest time of the three trials were included in analyses. Sex and height criteria were used to categorize gait speed. Slow gait speed was defined as ≥7 seconds for males (height ≤173 cm) and ≥6 seconds for male (height >173 cm). For females slow gait speed was defined as ≥7 seconds (height ≤159 cm) and ≥6 seconds (height >159 cm) (Fried et al., 2001).

Grip strength was measured using a standard, validated measure. Maximal grip strength was measured using a handgrip dynamometer (Jamar Hydraulic Hand Dynamometer, Patterson Medical, Warrenville, IL), with the best of three attempts recorded. The participants were asked to sit with their feet flat on the floor and have their elbow bent to 90 degrees. They were instructed to squeeze the dynamometer as hard as possible and stop squeezing after 2–3 seconds. The participants alternated right and left hands with 30 seconds between trials. Low grip strength was defined as the lowest 20% for sex and BMI (Fried et al., 2001). Low grip strength for males was defined as grip strength ≤29 kg for BMI quantile between 20.89 and 28.35 kg/m², grip strength ≤30 kg for BMI quantile between 28.46 and 31.48 kg/m² and 31.68–33.70 kg/m², and grip strength ≤32 kg for BMI quantile between 33.95 and 44.79 kg/m². Among female participants, low grip strength was defined as grip strength ≤17 for BMI quantile between 21.62 and 26.28 kg/m², grip strength ≤17.3 kg for BMI quantile between 26.32 and 28.89 kg/m², grip strength ≤18 kg for BMI quantile between 29.24 and 31.86 kg/m², and grip strength ≤21 kg for BMI quantile between 31.86 and 45.55 kg/m².

Exhaustion was assessed using two self-report questions adopted from the Center for Epidemiological Studies Depression (CES-D) questionnaire, as previously reported by Fried et al. (2001): (1) How many days in the past week did you feel like everything was an effort?; and (2) How many days in the past week did you feel like you could not get going?, with responses of 0 = none of the time or less than one day; 1 = 1-2 days; 2 = 3-4 days; 3 = 5–7 days. Participants that answered “2” or “3” were classified as having a positive exhaustion frailty characteristic (Fried et al., 2001). The overall CES-D questionnaire has a Cronbach’s alpha of 0.90, and the exhaustion question was 0.86 (Cosco et al., 2017).

To obtain the prevalence of unique sex-stratified frailty groupings, we adopted unique analytical combinations. Using the initial number of frailty characteristics collected (three), we calculated mutually exclusive groupings from combinations of one (e.g., only weakness), two (e.g., exhaustion + weakness), and three-characteristics, yielding seven unique groupings. Both the 2-characteristic and 1-characteristic grouped participants were deemed pre-frail, as described by Fried et al., while participants having all 3 of the characteristics were considered frail for this analysis (Fried et al., 2001). Participants with none of the frailty characteristics (exhaustion, weakness, or slowness) were deemed as robust/non-frail.

Statistical Analyses

Participants’ baseline characteristics and descriptive statistics were obtained for statistical analyses. Mean and standard deviation (SD) were reported for the continuous variables while frequency and percentages were documented for categorical measures. Fisher’s exact test was used to compare the frequencies of nominal variables and an ANOVA was used to compare independent continuous variables. Prevalence was calculated for total number of frailty characteristics and for each frailty grouping overall and stratified by sex and BMI. To examine the relations between clinical characteristics and frailty, a secondary analysis utilized a spearman rank correlation to examine the relations between ABI severity and continuous markers of frailty (grip strength and gait speed). Due to sample size considerations, a secondary multivariable linear regression analysis was conducted to examine the relationship between ABI and frailty (adjusting for age and sex) and was reported in the Supplemental Table 1. The choice for covariates was based on the modest sample size, and prior literature in this area suggesting that age and sex are major confounders (Singh et al., 2012).

All tests were two-tailed, and a p-value <.05 was considered statistically significant for all analyses. All analyses were performed using SAS Version 9.4 (SAS Institute Inc, 2023) (SAS Institute Inc.). Data presented as mean ± SD for continuous variables and frequencies (%) for discrete variables.

Results

Sample Characteristics

The Physical Activity Daily Trial included 167 participants of which 61 (36.5%) were excluded due to missing data on any of the frailty measures (Figure 1). Participants with missing data were more likely to be younger (64 ± 10.7 vs. 67 ± 8.3 years), White individuals (88.5%), Males (76.7%), weigh less (84 ± 2 kg vs. 91.8 ± 2 kg), and have a smaller number of individuals with a college education (40.0%).

Figure 1.

PAD Frailty participant flow chart.

The final sample consisted of 106 participants (Table 1), that included 37 (34.9%) females, 89 (84.8%) White individuals, 67 (64.4%) married adults, 58 (57.4%) retirees, and 43 (45.3%) college graduates or higher and an overall group mean age of 66.9 ± 8.3 years and mean BMI of 30.9 ± 5.2 kg/m². Pertaining to clinical conditions at baseline for this group, 71.7% and 41.5% self-reported having hypertension and diabetes, respectively. Other chronic conditions reported include dyslipidemia (57.6%), arthritis (49.1%), chronic pain (3.1%), osteoporosis (15.1%), and stroke (11.3%). In this sample, only 18.6% had never smoked with a greater proportion being former and current smokers (63.7% and 17.7%, respectively).

Table 1.

Participant Baseline Characteristics by Frailty Status.

	Total (n = 106)	Frailty (n = 3)	Pre-frailty (n = 57)	Robust/non-frail (n = 46)	p-value^a
Patient characteristics
Age (years)^b	66.9 ± 8.3	77.3 ± 6.4	65.7 ± 8.5	67.6 ± 7.8	0.04
Sex
Male	69 (65.1%)	1 (33.3%)	33 (57.9%)	35 (76.1%)	0.06
Female	37 (34.9%)	2 (66.7%)	24 (42.1%)	11 (23.9%)
Race
White	89 (84.8%)	3 (100.0%)	47 (82.5%)	39 (86.7%)	0.95
Black	9 (8.6%)	—	6 (10.5%)	3 (6.7%)
Others	7 (6.7%)	—	5 (7.0%)	3 (6.7%)
Marital status
Single	6 (5.8%)	—	4 (7.1%)	2 (4.4%)	0.25
Married	67 (64.4%)	3 (100.0%)	30 (53.6%)	34 (75.6%)
Divorced	24 (23.1%)	—	16 (28.6%)	8 (17.8%)
Others^c	7 (6.7%)	—	6 (10.7%)	1 (2.2%)
Level of education
≥ College degree	43 (45.3%)	2 (66.7%)	20 (40.0%)	21 (50.0%)	0.50
High school or lower	52 (54.7%)	1 (33.3%)	30 (60.0%)	21 (50.0%)
Employment
Employed	25 (24.8%)	—	11 (20.0%)	14 (32.6%)	0.03
Retired	58 (57.4%)	3 (100.0%)	28 (50.9%)	27 (62.8%)
Unemployed	3 (2.9%)	—	3 (5.5%)	—
Others	15 (14.9%)	—	13 (23.6%)	2 (4.7%)
Tobacco use
Never	19 (18.6%)	—	10 (18.2%)	9 (20.5%)	0.75
Former	65 (63.7%)	3 (100.0%)	33 (60.0%)	29 (65.9%)
Current	18 (17.7%)	—	12 (21.8%)	6 (13.6%)
Clinical characteristics
Body Mass Index (kg/m²)^b	30.9 ± 5.2	33.5 ± 2.7	31.7 ± 5.4	29.8 ± 4.9	0.29
Diabetes	44 (41.5%)	2 (66.7%)	25 (43.9%)	17 (36.9%)	0.41
Dyslipidemia	61 (57.6%)	1 (33.3%)	32 (56.1%)	28 (60.9%)	0.94
Stroke	12 (11.3%)	—	8 (14.0%)	4 (8.7%)	0.68
Chronic pain	34 (32.1%)	—	26 (45.6%)	8 (17.4%)	<0.01
Hypertension	76 (71.7%)	2 (66.7%)	38 (66.7%)	36 (78.3%)	0.41
Kidney disease	13 (12.3%)	—	7 (12.3%)	6 (13.0%)	1.00
Lung disease	20 (18.9%)	1 (33.3%)	12 (21.1%)	7 (15.2%)	0.43
Arthritis	52 (49.1%)	1 (33.3%)	31 (54.4%)	20 (43.5%)	0.45
Osteoporosis	16 (15.1%)	1 (33.3%)	10 (17.5%)	5 (10.9%)	0.29
Ankle Brachial Index^b	0.70 ± 0.19	0.71 ± 0.21	0.70 ± 0.18	0.71 ± 0.20	0.86

^ap-value ANOVA comparing levels of frailty across continuous measures among participants.

^bMean ± Standard deviation.

^cIncludes: widowed, living as married/partner, and others.

Prevalence of Pre-frailty and Frailty

The combined overall prevalence of pre-frailty (1-2 characteristics) and frailty (3 characteristics) was 56.6% with a greater proportion of participants being pre-frail than frail (53.8% vs. 2.8%). Participants characterized as robust accounted for 43.4% of the cohort (Table 1). Pre-frail individuals were younger than those individuals who were characterized as frail (65.7 ± 7.8 vs. 77.3 ± 6.4 years, respectively). Pre-frail individuals were mostly male (57.9%) and White (82.5%). Subsequent stratification by sex indicated a greater proportion of females were physically frail compared with males (66.7% vs. 33.3%). A larger proportion of males were also non-frail/robust compared with females (76.1% vs. 23.9%, respectively). Frail individuals with PAD had an average BMI of 33.5 ± 2.7 kg/m², while pre-frail adults had an average BMI of 31.6 ± 5.4 kg/m². Among the frail group, the most common chronic condition was diabetes (100%) followed by hypertension (66.7%). The most common chronic condition among pre-frail individuals was hypertension (66.7%), followed by dyslipidemia (56.1%).

Groupings of Frailty Characteristics

Among participants who met criteria for one or more frailty characteristic (n = 60) (Table 2), 5.0% of participants (n = 3) had all three frailty characteristics. Among those with two-frailty groupings (n = 14), weakness + slowness was the most prevalent grouping (10.0%) followed by exhaustion + weakness (8.3%) and exhaustion + slowness (5.0%). Among participants with one frailty characteristic, exhaustion was most common with a prevalence of 33.3%, whereas weakness was the second most common single characteristic at 20.0% followed by slowness at 18.3%.

Table 2.

Prevalence of Unique Frailty Groupings.

	Total (N = 60)	Males (n = 34)	Females (n = 26)
Exhaustion + Weakness + Slowness	3 (5.0%)	1 (2.9%)	2 (7.7%)
Exhaustion + Weakness	5 (8.3%)	2 (5.9%)	3 (11.5%)
Exhaustion + Slowness	3 (5.0%)	2 (5.9%)	1 (3.9%)
Weakness + Slowness	6 (10.0%)	4 (11.8%)	2 (7.7%)
Exhaustion	20 (33.3%)	15 (44.1%)	5 (19.2%)
Weakness	12 (20.0%)	6 (17.7%)	6 (23.1%)
Slowness	11 (18.3%)	4 (11.8%)	7 (26.9%)

When observing the unique groupings of frailty characteristics and stratifying by sex, 2.9% of males and 7.7% of females had all three available frailty characteristics. Of the 34 males with frailty characteristics, the most prevalent two-frailty grouping was weakness + slowness (11.8%) followed by both exhaustion + weakness and exhaustion + slowness (both 5.9%). Additionally, the most prevalent single characteristic among males was exhaustion (44.1%) followed by weakness and slowness (17.7% and 11.8%, respectively). Among the 26 females with frailty characteristics, the most prevalent two-frailty grouping was exhaustion + weakness (11.5%) followed by weakness + slowness (7.7%) and exhaustion + slowness (3.9%). The most prevalent single characteristic among females was slowness (26.9%) followed by weakness (23.1%) and exhaustion (19.2%).

Frailty Markers and ABI Severity

Table 3 displays the ABI across the different clusters for the sample and by sex. The most severe ABI overall occurred in the exhaustion + slowness followed by weakness + slowness. Table 4 displays the results of the Spearman rank correlation analysis, which examines the relationship between ABI and continuous markers of frailty among 102 participants with ABI measurement at baseline. The correlation coefficient between ABI and grip strength in the entire sample was 0.14 (p-value: 0.17). Among participants who were pre-frail, the correlation coefficient between ABI and grip strength was 0.12 (p-value: 0.39). In the overall sample, the correlation coefficient between ABI and gait speed was −0.22 (p-value: 0.03). However, among pre-frail participants, the correlation coefficient between ABI and gait speed was −0.40 (p-value: 0.003). Due to sample size considerations for the subgroup of frail participants, correlation analyses were restricted to robust and pre-frail participants alone. Among participants who were robust, the correlation coefficient between average ABI and grip strength was 0.13 (p-value: 0.40), the correlation coefficient between gait speed and average ABI was −0.01 (p-value: 0.95).

Table 3.

ABI of Unique Frailty Groupings.

	Total (N = 60)	Males (n = 34)	Females (n = 26)
Exhaustion + Weakness + Slowness	0.71 ± 0.21	0.81 ± 0.00	0.66 ± 0.27
Exhaustion + Weakness	0.83 ± 0.12	0.88 ± 0.02	0.78 ± 0.19
Exhaustion + Slowness	0.54 ± 0.04	0.54 ± 0.04	—
Weakness + Slowness	0.57 ± 0.15	0.58 ± 0.17	0.54 ± 0.00
Exhaustion	0.74 ± 0.16	0.76 ± 0.16	0.67 ± 0.12
Weakness	0.67 ± 0.23	0.63 ± 0.28	0.70 ± 0.18
Slowness	0.70 ± 0.15	0.77 ± 0.20	0.68 ± 0.13

Table 4.

Spearman Rank Correlation Coefficient for ABI and Markers of Frailty (n = 102).

	Overall	Pre-frail
	ABI
Grip strength	0.14 (0.17)	0.12 (0.39)
Gait speed	−0.22 (0.03)	−0.40 (0.003)

Rho (p-value), ABI: Ankle Brachial Index.

Relationship Between ABI and Frailty

Due to sample size consideration, a regression model examining the relationship between frailty and ABI combined both frailty and pre-frailty in a single category. Additionally, the relationship between frailty markers (compared to having none, or being robust), and ABI was also computed (Supplemental Table 1). There was no apparent relationship between ABI and frailty or frailty markers after adjusting for age and sex.

Discussion

The purpose of this study was to determine the demographic and clinical characteristics associated with frailty markers in addition to examining the prevalence of frailty groupings in a cohort of patients with PAD. We observed that the prevalence of pre-frailty was high in this cohort. We also found that the most common combination of frailty characteristics was weakness + slowness. Collectively, our data demonstrate that in this cohort of patients with PAD, pre-frailty, characterized by the most frequent combination of weakness + slowness, was the most prevalent in this study. Secondarily, we sought to determine the relationship of ABI values with markers of frailty and frailty status. We found that overall and among pre-frail participants, the presence of PAD (ABI <0.9) was correlated with slower gait speed.

In this cohort of patients with PAD, we observed the prevalence of pre-frailty was 53.8% of the sample, which is greater than the prevalence reported by Fried and colleagues in a general older population (Fried et al., 2001). Consistent with our estimates, other large cohorts of community-dwelling older adults (≥60 years of age) showed the prevalence of pre-frailty, measured using the Fried’s phenotypic criteria, was 35–60% (Danon-Hersch et al., 2012; Fernandez et al., 2014; Fried et al., 2001; Pujos-Guillot et al., 2018). The results of this study are consistent with the literature showing the prevalence of pre-frailty in a PAD cohort was higher than frailty (72.2% and 13.4%, respectively) (Farah et al., 2021). Among other older cardiac patients, several cross-sectional and longitudinal studies have identified pre-frailty as the most prevalent (Fernandes et al., 2021; Fernandez-Bolaños et al., 2008; Newman et al., 2001; Newman et al., 2006; Veronese et al., 2017a; Woods et al., 2005). Our findings add to the existing body of knowledge highlighting the increased prevalence of pre-frailty among patients with cardiac conditions—in particular PAD—and displays the importance of assessing pre-frailty in patients with known cardiovascular disease.

In our cohort of patients with PAD, pre-frailty was more prevalent among males, while frailty was more prevalent among females. Though our results are consistent with others showing sex differences in pre-frailty and frailty in both community-dwelling older adults and older cardiac patients (Collard et al., 2012; Davis et al., 2021; Denfeld et al., 2021; Gordon et al., 2017; Son et al., 2019), sex differences in relation to frailty remains poorly understood (Gordon & Hubbard, 2018). However, much of the clinical research has identified females as usually having greater prevalence of frailty and risk of frailty (Kane & Howlett, 2021), likely related to females having lower muscle strength and overall physical function compared to males (McDermott et al., 2011; Okabe et al., 2021; Pabon et al., 2022). Additionally, these differences may be due to a combination of other determinants of health, which have important consequences for the susceptibility to certain diseases, mortality, treatment, and other outcomes among older populations with chronic conditions. Preclinical models are useful to explore potential mechanisms contributing to the overall sex differences observed in pre-frailty and frailty. In mouse, dog, and non-human primates, females have exhibited greater frailty when using the preclinical frailty assessment tools (Kane & Howlett, 2021).

We also sought to explore the prevalence of combinations of frailty characteristics among this cohort of patients with PAD. The combination of weakness + slowness was the most prevalent group of characteristics among those who met the definition of pre-frailty, accounting for 10.0%. The combinations of exhaustion + weakness (8.3%) then exhaustion + slowness (5.0%) were the next most common groupings. When stratifying by sex, the most prevalent grouping among males was similar to the overall grouping of weakness + slowness, while the most prevalent grouping among females was exhaustion + weakness. Both weakness and slowness have been reported to be potential prognostic indicators of some cardiovascular events (PAD, stroke, and cardiovascular mortality) and both are associated with PAD in older individuals (Kuki et al., 2019). In a study of Saudi adults aged 60 years and older, females reported higher prevalence of exhaustion and weakness (Alqahtani et al., 2021), while a study of Spanish adults of the same age have shown a greater prevalence of exhaustion and slowness among females (Fernandez-Bolaños et al., 2008).

Though much is known of the impact of weakness, slowness, and exhaustion individually, future work is needed to determine any correlations between the grouping of the characteristics and cardiovascular outcomes. In using the definition of frailty outlined by Fried et al. (Fried et al., 2001), understanding the clustering of the frailty characteristics may provide valuable insight into how those individual components co-occur to impact the individual. Though this method of clustering is new in frailty and PAD, it has been previously used in other clinical populations (Booker et al., 2021; Rodriguez-Colon et al., 2009). Our data suggest weakness + slowness are frequently experienced together among patients with PAD who meet the definition of pre-frailty.

Pre-frailty has been associated with a 4-fold increased risk of becoming frail following a 5-year follow-up (Fried et al., 2001). Overall, the prevalence of pre-frailty was high, which may indicate that these pre-frail patients with PAD may have been on the verge of transitioning to frailty. It has been shown that pre-frailty is a reversible condition if it is addressed early (Fried et al., 2001) with different interventions, namely exercise interventions (e.g., high-intensity resistance training and combination of resistance and aerobic training) (Fiatarone et al., 1994; Villareal et al., 2011). However, progression to states of greater frailty is more common than transitions to states of lesser frailty (Gill et al., 2006). As such, consideration of additional determinants contributing to the transition from pre-frailty to frailty and how frailty status evolves over time in chronic populations will aid in the design of appropriate frailty interventions and identification of target populations to prevent, delay, or reverse progression.

Our secondary results are in line with and build upon previous studies showing relationships with lower ABI or a clinical diagnosis of PAD with objective measurements of impaired physical function, namely gait speed (Kuo & Yu, 2008; McDermott et al., 2013; McDermott et al., 2010). However, follow-up regression analyses of ABI and frailty markers and groupings were insignificant. This is likely due to the low sample size. There are a few potential mechanisms linking PAD to a reduction in function. The leg-specific mechanisms may include low blood perfusion of the leg muscles during activity, which contributes to some functional impairment related to PAD (Brunner et al., 2016). Additionally, in some PAD cases, muscular atrophy in the legs as a result of ongoing ischemia has been reported (Parmenter et al., 2013), which could have an impact on leg-specific function resulting in slower gait speed. While low ABI is an indicator of atherosclerosis, which can manifest into other CVDs (Gupta et al., 2014), it can lead to poor physical function. Furthermore, Martin-Ponce et al. (Martin-Ponce et al., 2014) reported individuals with lower ABI scores (<0.5) had an increased mortality rate in addition to malnutrition and impaired physical function, namely hand grip strength and six-minute walk test distance. Exercise-based interventions may improve ABI values and thus decrease mortality and improve physical function within the PAD population (Takatori et al., 2012). Further investigations are needed to determine the potential mechanistic links between frailty outcomes and ABI in addition to identifying targeted interventions to improve the quality of life in individuals with low ABI scores.

Several limitations of the study exist. First, the recruited sample may not represent the full spectrum of frailty characteristics to be observed among patients with PAD. Second, the study population was predominately white and thus further study is warranted in other racial/ethnic groups with PAD. Lastly, data on all five of the Fried characteristics were not collected including an estimate of physical activity and the occurrence of unintentional weight loss, although data on other frailty characteristics were collected via validated questionnaires and assessments.

Conclusions

These results provide valuable insight into the importance of assessing frailty and examining the clustering of frailty phenotypic characteristics among individuals with PAD. This may aid in identifying pre-frail populations at risk of becoming frail and developing frailty-prevention activities. The present study highlighted the prevalence of pre-frailty and frailty among a cohort of patients with PAD. We, also, identified unique combinations of frailty characteristics within this cohort. While previous studies have focused on the number of frailty characteristics, less is known of how the individual characteristics interact to affect the human. Findings from this study indicate that, in this cohort of patients with PAD, pre-frailty was the most common and the combination of weakness + slowness was the most prevalent grouping. We noted sex differences in frailty and pre-frailty and the combinations. Although the size of this cohort was limited, these data assist identification of potential pathways that may be contributing to the loss in physiologic reserves leading to an increase in vulnerability among patients with PAD. While maintaining that frailty is a dynamic process, our findings suggest opportunities for prevention. The study sets a precedence for future epidemiologic studies that will inform the development and implementation of interventions into clinical practice designed to assess, prevent, and reverse frailty among individuals with chronic conditions, namely, PAD.

Supplemental Material

Supplemental Material - Assessment of Frailty Among Older Adults in the Physical Activity Daily Trial

Supplemental Material for Assessment of Frailty Among Older Adults in the Physical Activity Daily Trial by Raymond Jones, Ene M. Enogela, Emily L. Zumbro, Phonchit Soukhamneut, Caroline R. Richardson, Thomas W. Buford, and Elizabeth A. Jackson in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This publication was supported by the National Institutes of Health, National Heart, Lung and Blood Institute under Award Number R01AG045136 (EAJ, CR). Additional support for this manuscript was received from the National Institutes of Health under Award Numbers K12HL143958 (RJ), P30AG050886-09S1 (ELZ). Effort for RJ, EME, ELZ, and TWB for this work was also partially supported by the UAB Center for Exercise Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

IRB Protocol Approval Number

University of Alabama at Birmingham IRB-300001012.

Clinical Trials

ClinicalTrials.gov: NCT02022423.

ORCID iD

Raymond Jones

Supplemental Material

Supplemental material for this article is available online.

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