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Minimal correlation between physical exercise capacity and daily activity in patients with intermittent claudication
Minimal correlation between physical exercise capacity and daily activity in patients with intermittent claudication Lindy N. M. Gommans, MD, PhD,a,b David Hageman, MD,a,b Ingeborg Jansen, MD,a Robbin de Gee, MSc,a Rob C. van Lummel, MSc,c Nicole Verhofstad, MSc, PhD,a Marc R. M. Scheltinga, MD, PhD,d,e and Joep A. W. Teijink, MD, PhD,a,b Eindhoven, Maastricht, Amsterdam, and Veldhoven, The Netherlands Background: Walking capacity measured by a treadmill test (TT) reflects the patient’s maximal capacity in a controlled setting and is part of the physical exercise capacity (PEC). Daily physical activity (PA) is defined as the total of actively freely produced movements per day. A lower PA level has been increasingly recognized as a strong predictor of increased morbidity and mortality in patients with intermittent claudication (IC). Recent insights suggested that an increased PEC does not automatically lead to an increase in daily PA. However, the precise relation between PEC and PA in patients with IC is still unclear. Methods: A cross-sectional study was conducted to assess the association between several PEC outcomes and PA in a general IC population. PEC was determined by well-established tests (Gardner-Skinner TT, a physical performance battery, a timed up-and-go test, and a 6-minute walk test distance). PA was obtained during 7 consecutive days using a triaxial accelerometer (Dynaport MoveMonitor; McRoberts BV, The Hague, The Netherlands). Five PA components (lying, sitting, standing, shuffling, and locomotion) and four parameters (total duration, number of periods, mean duration per period, and mean movement intensity per period) were analysed. Correlation coefficients between PEC and PA components were calculated. Results: Data of 46 patients were available for analysis. Patients were sedentary (sitting and lying) during 81% of the day and were physically active (standing, shuffling, and locomotion) for the remaining 19% of the time. Correlations between PEC outcomes and PA ranged from very weak (0.025) to moderate (0.663). Moderate correlations (as therefore assumed to be relevant) were only found for outcomes of both the TT and 6-minute walk test and the locomotion components of PA. For instance, functional claudication distance (measured by TT) and number of steps per day correlated reasonably well (Spearman correlation r [ 0.663; P < .01). Conclusions: Exercise capacity and PA correlate minimally in patients with IC. PA may be preferred as a novel outcome measure and future treatment target in patients with IC. (J Vasc Surg 2016;-:1-7.)
Intermittent claudication (IC) is the most common symptom of peripheral artery disease (PAD) and results from atherosclerosis in large peripheral arteries.1 Among conservative therapies, supervised exercise therapy (SET) was found to be the most effective tool in decreasing IC symptoms and increasing physical exercise capacity (PEC).2 The term PEC actually reflects what a person is From the Department of Vascular Surgery, Catharina Hospital, Eindhovena; the Department of Epidemiology, CAPHRI School for Public Health and Primary Care, University Maastricht, Maastrichtb; the Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdamc; the Department of Vascular surgery, Maxima Medical Center Veldhoven, Veldhovend; and the CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht.e Author conflict of interest: R.C.v.L. is owner of McRoberts BV, Den Haag, The Netherlands, distributor of the DynaPort MoveMonitor. Correspondence: Joep A. W. Teijink, MD, PhD, Department of Vascular Surgery, Catharina Hospital Eindhoven, PO Box 1350, Eindhoven 5602 ZA, The Netherlands (e-mail: [email protected]). The editors and reviewers of this article have no relevant financial relationships to disclose per the JVS policy that requires reviewers to decline review of any manuscript for which they may have a conflict of interest. 0741-5214 Copyright Ó 2016 by the Society for Vascular Surgery. Published by Elsevier Inc. http://dx.doi.org/10.1016/j.jvs.2015.10.060
maximally capable of in a controlled laboratory setting.3 In patients with IC, PEC is usually determined by a standardized treadmill test (TT).4 With this test, a physician is able to evaluate a patient’s individual walking capacity (ie, maximum walking distance), which is especially useful to determine the effect of SET.4 TT-based walking capacity is frequently used to assess the effect of novel treatment strategies in clinical trials. A recent study demonstrated that an improvement in PEC does not automatically lead to an increase in daily physical activity (PA).5 These insights are in line with a recent study that also found no increase in daily PA levels after SET in patients with IC.6 These results may have important implications because the daily PA level is a strong predictor of morbidity and mortality in patients with IC.7 Several studies have now demonstrated that patients with IC exhibit lower daily PA levels than healthy controls8,9 and that most do not meet the internationally recommended standard for PA.8 The World Health Organisation defines PA as “any bodily movement produced by skeletal muscles that requires energy expenditure.”10 The different aspects of PA include mode, frequency, duration, intensity, and context in which the activities occur. Hence, daily PA reflects the total of actively produced movements a person actually performs daily. 1
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In current practice, PA levels are most commonly determined by interviews or questionnaires. These self-report tools proved to be poorly reproducible11 and seem insensitive for assessing IC symptoms.12 Despite the current possibility to objectively measure PA with the use of activity monitors (ie, pedometers and accelerometers), this approach is rarely applied in daily clinical practice although such devices are small, easily wearable, and found to provide reliable information on PA.13 Patients only need to wear the device for multiple days to obtain an accurate measure of daily PA levels, because PA can fluctuate over days.14 For both research and clinical purposes, it might be important to focus more on daily PA as an outcome parameter.7,15 Whether PEC outcomes, however, could serve as a surrogate marker for PA is currently unknown in patients with IC because the relation between PEC and daily PA is still unclear. For chronic obstructive pulmonary disease (COPD), research has showed that PEC and daily PA are two completely different entities.16 These results underscored the importance of measuring daily PA to optimize treatment for COPD, which might be the same for IC patients. The current study therefore aims to evaluate how different PEC measures relate to daily PA in a general IC population. In line with previous results obtained from a COPD population, we hypothesized that PEC outcomes are poorly associated with daily PA behavior.
PA. All participants were extensively instructed how to wear the PA device properly. Patients returned to our hospital once for the PEC measurements. Assessment of PEC and PA was scheduled in random order. Four researchers (L.G., I.J., R.G., and D.H.) were responsible for the data collection. Measurements ABIs. According to standardized institutional protocols, ABIs were measured by experienced nurses at the Catharina Hospital Vascular Laboratory. A hand-held 8MHz Doppler probe was used to measure systolic pressures at the brachial artery of both arms, followed by the posterior tibial artery or dorsal pedal artery at the level of both ankles. A baseline measurement was performed at rest. Subsequently, a postexercise ABI was obtained (ie, TT at 3.2 km/h and a 6 slope for a maximum of 6 minutes). The ABI was calculated by dividing the highest obtained ankle pressure by the highest obtained arm pressure. PEC. PEC was determined through a sequence of tests. Patients performed a TT, a short physical performance battery (SPPB), a timed up-and-go (TUG) test, and a 6-minute walk test (6-MWT). d
METHODS All procedures in this study were approved by the Catharina Hospital Medical Ethical Committee, Eindhoven, The Netherlands. Participants Between February and May 2015, patients with symptoms of IC were recruited at the vascular surgery outpatient clinic of the Catharina Hospital and from two physical therapy centers, one in Eindhoven and the other in Veenendaal (The Netherlands). Inclusion criteria were PAD Rutherford stage 1 to 3 and an ankle-brachial index (ABI) <0.90 at rest or a drop of >0.15 after a graded TT, or both, or a duplex or magnetic resonance angiography (MRA) demonstrating a significant arterial stenosis (>50%). Exclusion criteria were serious cardiopulmonary comorbidities (New York Heart Association Functional Classification III-IV), PAD Rutherford stage 4 to 6, previous lower limb amputation, use of walking aids, high probability of nonadherence to the protocol (eg, for instance due to dementia), other comorbidities that might limit the patient’s walking ability (eg, severe arthritis, Parkinson disease, recent trauma to the lower extremities), and wearing the PA device for <5 days because that is the minimum number of days for obtaining accurate data on locomotion bouts.17 Patients who were willing to participate were counseled regarding the study protocol before providing written informed consent. Study protocol A cross-sectional design was used to investigate the relation between PEC outcomes and components of daily
d
d
A standard TT was based on the graded GardnerSkinner protocol.18 Patients began walking on a flat treadmill with a constant speed of 3.2 km/h. The incline was increased by 2% every 2 minutes until a maximum of 10% was reached. The test ends at a maximum of 30 minutes (1600 meters). The functional claudication distance (FCD) and absolute claudication distance (ACD) were recorded. FCD was defined as the distance at which a patient prefers to stop because of claudication symptoms.19 ACD was defined as the maximal walking distance limited by intolerable pain. The SPPB, developed by Guralnik et al,20 is a threetest battery that assesses general lower extremity function. In a 3-meter walking velocity test, patients were asked to walk 3 meters at their usual speed. Participants performed the test twice, and the fastest result was selected. In a repeated chair rise test, patients were instructed to stand up and sit down five times as fast as possible with arms folded across the chest. The time for completing the test was recorded, and results generally reflect leg strength and balance.20 In the standing balance test, patients were asked to maintain balance for 10 seconds in the following three positions: feet together (side-by-side position), heel of one foot next to the big toe of the other foot (semitandem position), and the heel of one foot in front of the toe of the other foot (tandem position). A maximum of 4 points can be scored in each of these three tests, leading to a 12-point maximum score. During a TUG test, patients were instructed to rise from a chair, walk 3 meters, turn around, walk back to sit again, all at their fastest pace. The total time was recorded.
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A 6-MWT was based on the standardized Montgomery and Gardner protocol.21 Patients were asked to walk up and down a 20-meter distance for 6 minutes, covering as much distance as possible. Total walking distance and the number of stops due to IC symptoms were registered.
Daily PA. PA was measured using the Dynaport MoveMonitor (MM; McRoberts BV, The Hague, The Netherlands), a triaxial seismic accelerometer. This device has been validated to objectively measure PA in patients with IC.13 The MM consists of a rechargeable battery, USB connection, and raw data storage capability on a microsecure digital card. Participants wore the MM on a belt around the waist for 7 consecutive days, except when showering or bathing. The MM includes three orthogonal accelerometers that measure acceleration in three axesdx (longitudinal), y (mediolateral), and z (anterior-posterior)dat a sample rate of 100 samples per second. Raw data were processed using pattern recognition algorithms (MM 1.0.7.21 analysis software) and expressed in terms of body posture (ie, lying and sitting), locomotion, and movement parameters (ie, movement duration, intensity, and frequency). The PA categories retrieved were lying, sitting, standing, shuffling, and locomotion. Shuffling separation divides the active (not walking) parts into two categories: shuffling and transitions. Shuffling is defined as all movement from point A to point B that is not walking. Thus, if the number of steps is fewer than three, or the intensity and direction of the motion do not comply with the characteristics of walking, the movements are classified as shuffling.13 Four subsequent parameters were reported for each activity category: total duration, number of periods, mean duration per period and the weighted mean movement intensity (MI) per period. Data analysis Continuous variables are expressed as means 6 standard deviation when normally distributed and as medians with interquartile ranges (IQRs) in the case of a skewed distribution. Categoric variables are presented with percentages. Normality checks were performed visually using histograms and with a Kolmogorov-Smirnov test. The relation between daily PA (variables being lying, sitting, standing, shuffling and locomotion) and PEC (variables being the outcomes measures of a TT, SPPB, TUG, and 6MWT) was determined by calculating Spearman correlation coefficients (r) because PEC data revealed a skewed distribution. According to Zou et al,22 correlations of $0.50 and $0.80 were considered as, respectively, moderate and strong associations. Multivariate regression analysis was conducted to study the potential influence of gender and age on the relation between PEC and PA. Data were analyzed using SPSS 21.0 software (IBM, Armonk, NY, USA). A P value of <.05 was regarded as being statistically significant. Sample size calculation (with an a ¼ .05 and a
Gommans et al 3
b ¼ .80) revealed a sample size of 46 participants was needed to realize statistical significant correlations of $0.4 between PEC and PA. RESULTS Recruitment and response. A total of 55 patients met eligibility criteria and were willing to participate. Inclusion of nine patients was based on duplex or MRA images. Nine patients (w17%) were excluded because the MM was worn <5 days (n ¼ 2), technical failure (n ¼ 1), withdrawal due to personal reasons (n ¼ 4), and unanticipated operative interventions during the MM measurement period (n ¼ 2). Therefore, complete data sets of 46 patients were available for analysis. Sample characteristics. Baseline characteristics are summarized in Table I. Mean age was 66 6 9 years, 35 participants (76%) were men, and 21 (46%) demonstrated bilateral complaints. The lowest mean ABI at rest was 0.63 6 0.17, dropping to 0.38 6 0.19 after exercising. Cardiovascular risk factors (smoking, diabetes mellitus) were commonly observed (Table I). PEC. The median FCD and ACD on the TT were 360 meters (IQR, 160.00-536.50 meters) and 438 meters (IQR, 298.25-714.50 meters), respectively. Six patients (13%) achieved the maximum walking distance of 1600 meters. The median walking distance assessed by a 6-MWT was 400 meters (IQR, 320.50-468.50 meters). For the TUG test, a median of 7.58 seconds for completion was found. A total of 87% patients attained a SPPB total score of 10 of 12 (range, 8-12). The SPPB subtests revealed that 76% had a maximum score for balance, 98% had a maximum score for walking speed, but just 18% scored maximal points on the repeated chair rise test (Table II). PA in IC patients. Patients were physically active for 4.46 hours per day (standing, shuffling, locomotion), equivalent to 19% of a 24-hour day. The mean number of hours that patients spent in a sedentary mode (lying or sitting) was 18.98, corresponding with the remaining 81% of a day. Note that this also includes sleeping overnight. Patients initiated a median number of 408 (IQR, 264-611) locomotion periods per day, with a mean MI of 0.187 6 0.022 m/s2 and a median time of 9.88 seconds (IQR, 8.81-12.02 seconds) per period. The median number of transitions from sitting to standing was 52 (IQR, 40-69). A detailed overview of the median daily PA levels is summarized in Table III. Correlation between PEC and daily PA. No significant or relevant correlations (r $ 0.4) were present between SPPB/TUG test outcomes and any of the PA components (data not shown), except for a weak inverse correlation (r ¼ 0.492; P < .01) between locomotion MI and the results of the TUG test. ACD (using a TT) and duration of locomotion per day were moderately correlated (r ¼ 0.561; P < .01). In contrast, corridor-based walking distance (using a 6MWT) and duration of locomotion per day showed just a weak correlation (r ¼ 0.375; P < .01). Stronger correlations were present between total number of steps per day
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Table I. Baseline characteristics of the study population Characteristics Male gender Age, years Body mass index, kg/m2 Affected side Left Right Both Lowest ABIa Rest After exercising Comorbidity Pulmonary Cardiac TIA/stroke Diabetes mellitus Hypertension Current smokers
No. (%) or mean 6 SD (N ¼ 46) 35 (76) 66 6 9 25.1 6 4.9 16 (35) 9 (20) 21 (45) 0.63 6 0.17 0.38 6 0.19 7 10 5 10 32 14
(15) (22) (11) (22) (70) (30)
ABI, Ankle-brachial index; SD, standard deviation; TIA, transient ischemic attack. a Based on ABI values of 37 patients with intermittent claudication (IC).
Table II. Physical exercise capacity (PEC) of the study population Exercise assessment TT FCD, meters ACD, meters 6-MWT distance, meters TUG, seconds SPPB Balance (1-4) Velocity (1-4) Chair rise test (1-4) Total score (1-12)
Median (IQR) 360 438 400 7.58 4 4 3 11
(160-537) (298-715) (321-469) (6.66-9.86) (3-4) (4-4) (2-3) (10-12)
6-MWT, 6-Minute walk test; ACD, absolute claudication distance; FCD, functional claudication distance; IQR, interquartile range; SPPB, short physical performance battery; TT, treadmill test; TUG, timed up and go test.
and TT outcomes (FCD: r ¼ 0.663; ACD: r ¼ 0.621; P < .01 for both). The outcome of the 6-MWT was moderately associated with locomotion MI (r ¼ 0.599; P < .01). The sedentary activity modes did not show any relevant correlations with TT or 6-MWT outcomes (Table IV). Multivariate regression analysis did not reveal any significant contribution of gender or age to the PEC and PA correlations. DISCUSSION In this study we investigated the relation between PEC outcomes and daily PA in patients with IC. Overall, only weak to moderate correlations between these parameters were found. A substantial portion of the correlations was therefore considered of minor relevance (r # 0.5), despite their statistical significance.22 Among relevant correlations, results of a TT (ie, FCD and ACD) showed strongest association with locomotion (ie, total amount of locomotion
and duration of locomotion per period). These findings possibly imply that “the better a TT performance, the more a patient walks in daily life.” Similar conclusions may be drawn from the correlations between TT-based FCD and ACD and number of steps per day. A moderate correlation was also found between a 6-MWT distance and locomotion MI. MI as determined by the acceleration and declaration signals indicates movement power. A comparison between corridor-based walking and treadmill walking demonstrated reduced anterior-posterior ground reaction forces during the latter protocol.23 Therefore, the results of MI and 6-MWT may be expected to demonstrate stronger correlations compared with MI and TT outcomes. Other authors have also reported that 6-MWT results correlated well with PA levels as expressed in general “activity units.”24 Correlations of FCD and PA were generally stronger then the correlations between ACD and PA. It may therefore be proposed that a measurement of FCD is currently the most optimal predictor of daily walking activity. Understanding of the underlying motives of PA behavior is an aid for optimizing intervention strategies.25 This assumption may particularly be true in sedentary populations including IC patients8 because the health benefits of increased PA are likely substantial. However, PA is a complex end point that is influenced by a range of factors (eg, personal, exercise-related, environment, and political decisions).25 Moreover, there is still uncertainty about the exact factors defining someone’s behavior to be physically active. A poor relation between PEC and PA, as shown in this study, may be not surprising because it was also demonstrated in other populations that included healthy elderly individuals14 and COPD patients.16 The concepts of PEC and PA may therefore be different.26,27 Our results indicate that the parameter PEC is able to predict 44% of the variance in PA (FCD vs numbers of steps per day) at best, leaving more than half for other factors to explain PA behaviour. Considering these results and knowing that a low PA level is a strong risk factor for developing comorbidities,7 assessment of PA as an additional outcome measure may have important implications for IC care. Correct assessment of PA in patients with IC seems to be a challenge. Questionnaires may help to identify a severely inactive patient, but these tools largely fail to provide a correct reflection of an entire patient population. Moreover, self-reported responses on daily PA are subject to social desirability bias.28 In contrast, activity monitors were found to be reliable tools for PA assessment and are currently widely available.13 These devices are easy to wear, and the number of problems that were encountered in the present study was minimal. A unique aspect of our methodology was the quantification of a range of different activity components (eg, sitting and standing) rather than more general measures of PA (eg, activity units or metabolic equivalents of a task).24,29 A precise measurement of PA is important because recent insights revealed that prolonged sitting (ie, sedentary behavior)
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Table III. Physical activity (PA) outcomes of the study population Duration hours per day, mean 6 SD
Outcomes Lying Sitting Standing Shuffling Locomotion
10.1 8.52 2.67 0.40 1.30
Activity time per day, mean 6 SD, hoursc Sedentary time per day, mean 6 SD, hoursc,d No. of sit-to-stand, median (IQR) No. of steps per day, median (IQR) MM not worn per day, mean 6 SD, minutesc
6 6 6 6 6
No. of periods per day, median (IQR)
1.92 1.87 1.19 0.26 0.72
9 109 756 409 408
(6-15) (90-141) (528-1202) (277-686) (264-611)
Time per period, seconds or minutes, mean (IQR) 60.8 (41.6-91.6)a 4.38 (3.0-6.1)a 11.02 (9.4-15.9)b 2.77 (2.6-3.0)b 9.88 (8.8-12.0)b
MI, m/s2, mean 6 SD 0.005 0.023 0.052 0.139 0.187
6 6 6 6 6
0.002 0.013 0.012 0.028 0.022
4.46 6 2.0 18.98 6 1.9 52 (40-69) 6315 (3364-9207) 36.9 6 62.1
IQR, Interquartile range; MI, movement intensity; MM, Dynaport MoveMonitor; SD, standard deviation. a Minutes. b Seconds. c Add up to 100%. d Note that the sedentary time also includes sleeping overnight.
Table IV. Spearman correlation coefficients between physical exercise capacity (PEC) and physical activity (PA) TT Variable Lying Duration No of periods Time/period Intensity Sitting Duration No. of periods Time per period Intensity Standing Duration No. of periods Time per period Intensity Shuffling Duration No. of periods Time per period Intensity Locomotion Duration No. of periods Time per period Intensity Steps per day Total No.
FCD, r
ACD, r
6-MWT distance, r
0.173 0.297a 0.306a 0.230
0.138 0.245 0.257 0.190
0.234 0.137 0.114 0.029
0.163 0.305 0.116 0.103
0.196 0.153 0.183 0.025
0.060 0.303a 0.204 0.157
0.253 0.439b 0.459b 0.251
0.281 0.422b 0.387b 0.286
0.228 0.343a 0.245 0.359a
0.449b 0.431b 0.088 0.094
0.438b 0.417b 0.093 0.168
0.406b 0.356a 0.256 0.237
0.617b 0.463b 0.596b 0.277
0.561b 0.422b 0.549a 0.323a
0.375a 0.245 0.499a 0.599b
0.663b
0.621b
0.425b
6-MWT, 6-Minute walk test; ACD, absolute claudication distance; FCD, functional claudication distance; TT, treadmill test. a Statistically significant at P < .05. b Statistically significant at P < .01.
predicts all-cause mortality, independent of these overall PA levels expressed in metabolic equivalents of a task (ie, in MET).30 Prolonged sitting disrupts metabolic functions, resulting in increased plasma triglyceride levels,
decreased levels of high-density lipoprotein cholesterol, and decreased insulin sensitivity.30 Results from molecular biology and medical chemistry studies show that PA and sedentary behavior are thought to have different influences on the body, supporting their independent effects on health.31 Consequently, the positive effect of (vigorous) PA a couple of times per week (for instance during SET) does not seem to compensate for the independent negative effect when one is predominantly sedentary for the remaining time. Thus, incorporating different activity components of PA in patients with IC, in particular, time spent in sedentary modes, may be more predictive compared with measurements of just energy consumption. Previous studies failed to demonstrate beneficial effects of SET on daily PA in patients with IC. Fokkenrood et al6 and Crowther et al32 both concluded that PA levels did not increase after 3 and 12 months of SET, respectively. The finding that an exercise program primarily focusing on stimulating physical capacity does not improve daily PA is explainable. When PAD gradually progresses over the years, the patient likely develops a sedentary lifestyle requiring more than just an increase in exercise capacity to change. This explanation is in line with a previous conclusion from a COPD study.5 Exercise interventions require targeting to exercise capacity as well as to behavior change with regard to daily PA to achieve improvement in PA.5 Techniques to change behavior, such as motivational interviewing, might be helpful in achieving and maintaining improved PA levels. A brief psychologic intervention of just two 1-hour sessions significantly increased daily walking in patients with IC,33 an effect that was sustained for >2 years.34 SET provides an ideal opportunity for incorporation of such psychologic interventions (ie, health behavior-changing techniques), thereby probably optimizing SET programs and improving patient outcome. So, high-quality trials examining the effectiveness of these health behavior-changing techniques in patients with IC are urgently needed.35 The additional value of health
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technology applications (eg, integrated in smart phones) for enhancing PA also requires detailed studies. Based on present results, one may argue that measurement of PEC does not have an additional role in an improved understanding of daily PA in patients with IC. However, such a functional assessment was found to reflect the IC patients’ abilities and limitations36 and may therefore provide valuable information. Walking distance, as measured using a TT, evidently has limitations, because discrepancies between treadmill measures and perceived walking impairment are reported.37 But, it must also be appreciated that treadmill-based walking distance is the most widely used outcome parameter to evaluate treatment effects of SET in clinical trials and daily practice.36 The TUG and SPPB measures may both be of less relevance or suitable for this patient population. The high number of maximum scores (“ceiling effect”) implies that patients were not really challenged during these tests. Both protocols require just a very short period of activity (usually <15 seconds), whereas most IC patients experience pain at a later time. PEC, considered to determine someone’s maximal capacity, may therefore be inadequately estimated by these tests, as also indicated by the lack of correlations with PA. Consequently, we propose to incorporate PA as a novel outcome measure and treatment goal for IC alongside current walking (TT or 6-MWT) PEC measurements. The present study must be interpreted in view of the fact that PEC results may be overestimated because people are generally thought to push to their limits when challenged during a test setting compared with a normal dayto-day situation. It may therefore be possible that this has influenced correlation strength. Moreover, several factors apart from age and gender might influence potential correlations between PEC and PA. Variance in the use of medication, presence of comorbidities, and body mass index, for instance, have a possible effect on PA, and correlations with PEC might be different for certain subgroups of patients with IC. Unfortunately, the study’s cross-sectional design did not allow for studying causality. Future research should preferably explore determinants which possible contribute to PA. Such an analysis may allow for a more selective use of monitoring equipment. CONCLUSIONS Walking distance as determined with a 6-MWT or graded TT protocol revealed weak to moderate correlations with locomotion components of PA, whereas no to weak correlations were found with sedentary PA components. On the basis of the present study results, the sedentary nature of IC patients, and the increased mortality risks accompanied with these low PA levels, we suggest including objectively measured PA as an additional outcome measure and treatment target in patients with IC. We thank Dirk van Dooren for his support in the data analysis and also greatly appreciate the help of Natasja Huls (physical therapist at Fysiotherapie De Jong, Eindhoven, The Netherlands) and Sander van de Hoef (Paramedisch
Instituut Rembrandt, Veenendaal, The Netherlands) for the recruitment of study participants. AUTHOR CONTRIBUTIONS Conception and design: LG, IJ, RG, RL, NV Analysis and interpretation: LG, IJ, RG, RL, MS, JT Data collection: LG, DH, IJ, RG Writing the article: LG, IJ, NV, MS, JT Critical revision of the article: LG, RG, RL Final approval of the article: LG, DH, IJ, RG, RL, NV, MS, JT Statistical analysis: LG, IJ, RL Obtained funding: Not applicable Overall responsibility: JT REFERENCES 1. Cooke JP, Chen Z. A compendium on peripheral arterial disease. Circ Res 2015;116:1505-8. 2. Society for Vascular Surgery Lower Extremity Guidelines Writing Group, Conte MS, Pomposelli FB, Clair DG, Geraghty PJ, McKinsey JF, Mills JL, et al. Society for Vascular Surgery practice guidelines for atherosclerotic occlusive disease of the lower extremities: management of asymptomatic disease and claudication. J Vasc Surg 2015;61(3 Suppl):2S-41S. 3. Goldstein RE. Exercise capacity. In: Walker HK, Hall WD, Hurst JW, editors. Clinical methods: the history, physical, and laboratory examinations. 3rd edition. Boston, MA: Butterworths; 1990. 4. Hiatt WR, Rogers K, Brass EP. Response to McDermott. Circulation 2014;130:68. 5. Zwerink M, van der Palen J, van der Valk P, Brusse-Keizer M, Effing T. Relationship between daily physical activity and exercise capacity in patients with COPD. Respir Med 2013;107:242-8. 6. Fokkenrood HJ, Lauret GJ, Verhofstad N, Bendermacher BL, Scheltinga MR, Teijink JA. The effect of supervised exercise therapy on physical activity and ambulatory activities in patients with intermittent claudication. Eur J Vasc Endovasc Surg 2015;49:184-91. 7. Garg PK, Tian L, Criqui MH, Liu K, Ferrucci L, Guralnik JM, et al. Physical activity during daily life and mortality in patients with peripheral arterial disease. Circulation 2006;114:242-8. 8. Lauret GJ, Fokkenrood HJ, Bendermacher BL, Scheltinga MR, Teijink JA. Physical activity monitoring in patients with intermittent claudication. Eur J Vasc Endovasc Surg 2014;47:656-63. 9. Gardner AW, Montgomery PS, Scott KJ, Afaq A, Blevins SM. Patterns of ambulatory activity in subjects with and without intermittent claudication. J Vasc Surg 2007;46:1208-14. 10. World Health Organization. Physical activity. Fact Sheet No. 385, updated January 2015. Available at: http://www.who.int/mediacentre/ factsheets/fs385/en/. Accessed September 8, 2015. 11. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 2003;37:197-206; discussion: 206. 12. Gardner AW, Katzel LI, Sorkin JD, Bradham DD, Hochberg MC, Flinn WR, et al. Exercise rehabilitation improves functional outcomes and peripheral circulation in patients with intermittent claudication: a randomized controlled trial. J Am Geriatr Soc 2001;49:755-62. 13. Fokkenrood HJ, Verhofstad N, van den Houten MM, Lauret GJ, Wittens C, Scheltinga MR, et al. Physical activity monitoring in patients with peripheral arterial disease: validation of an activity monitor. Eur J Vasc Endovasc Surg 2014;48:194-200. 14. Nicolai S, Benzinger P, Skelton DA, Aminian K, Becker C, Lindemann U. Day-to-day variability of physical activity of older adults living in the community. J Aging Phys Act 2010;18:75-86. 15. Gardner AW, Montgomery PS, Parker DE. Physical activity is a predictor of all-cause mortality in patients with intermittent claudication. J Vasc Surg 2008;47:117-22. 16. Fastenau A, van Schayck OC, Gosselink R, Aretz KC, Muris JW. Discrepancy between functional exercise capacity and daily physical
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activity: a cross-sectional study in patients with mild to moderate COPD. Prim Care Respir J 2013;22:425-30. van Schooten KS, Rispens SM, Elders PJ, Lips P, van Dieen JH, Pijnappels M. Assessing physical activity in older adults: required days of trunk accelerometer measurements for reliable estimation. J Aging Phys Act 2015;23:9-17. Hiatt WR, Hirsch AT, Regensteiner JG, Brass EP. Clinical trials for claudication. Assessment of exercise performance, functional status, and clinical end points. Vascular Clinical Trialists. Circulation 1995;92:614-21. Kruidenier LM, Nicolai SP, Willigendael EM, de Bie RA, Prins MH, Teijink JA. Functional claudication distance: a reliable and valid measurement to assess functional limitation in patients with intermittent claudication. BMC Cardiovasc Disord 2009;9:9. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 1994;49:M85-94. Montgomery PS, Gardner AW. The clinical utility of a six-minute walk test in peripheral arterial occlusive disease patients. J Am Geriatr Soc 1998;46:706-11. Zou KH, Tuncali K, Silverman SG. Correlation and simple linear regression. Radiology 2003;227:617-22. Riley PO, Paolini G, Della Croce U, Paylo KW, Kerrigan DC. A kinematic and kinetic comparison of overground and treadmill walking in healthy subjects. Gait Posture 2007;26:17-24. McDermott MM, Ades PA, Dyer A, Guralnik JM, Kibbe M, Criqui MH. Corridor-based functional performance measures correlate better with physical activity during daily life than treadmill measures in persons with peripheral arterial disease. J Vasc Surg 2008;48:1231-7. 1237.e1. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW, et al. Correlates of physical activity: why are some people physically active and others not? Lancet 2012;380:258-71. Leidy NK. Using functional status to assess treatment outcomes. Chest 1994;106:1645-6. Larson JL. Functional performance and physical activity in chronic obstructive pulmonary disease: theoretical perspectives. COPD 2007;4: 237-42.
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28. Adams SA, Matthews CE, Ebbeling CB, Moore CG, Cunningham JE, Fulton J, et al. The effect of social desirability and social approval on self-reports of physical activity. Am J Epidemiol 2005;161:389-98. 29. Clarke CL, Holdsworth RJ, Ryan CG, Granat MH. Free-living physical activity as a novel outcome measure in patients with intermittent claudication. Eur J Vasc Endovasc Surg 2013;45:162-7. 30. van der Ploeg HP, Chey T, Korda RJ, Banks E, Bauman A. Sitting time and all-cause mortality risk in 222 497 Australian adults. Arch Intern Med 2012;172:494-500. 31. Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N. Too little exercise and too much sitting: inactivity physiology and the need for new recommendations on sedentary behavior. Curr Cardiovasc Risk Rep 2008;2:292-8. 32. Crowther RG, Spinks WL, Leicht AS, Sangla K, Quigley F, Golledge J. Effects of a long-term exercise program on lower limb mobility, physiological responses, walking performance, and physical activity levels in patients with peripheral arterial disease. J Vasc Surg 2008;47:303-9. 33. Cunningham MA, Swanson V, O’Carroll RE, Holdsworth RJ. Randomized clinical trial of a brief psychological intervention to increase walking in patients with intermittent claudication. Br J Surg 2012;99: 49-56. 34. Cunningham MA, Swanson V, Holdsworth RJ, O’Carroll RE. Late effects of a brief psychological intervention in patients with intermittent claudication in a randomized clinical trial. Br J Surg 2013;100:756-60. 35. Galea MN, Weinman JA, White C, Bearne LM. Do behaviour-change techniques contribute to the effectiveness of exercise therapy in patients with intermittent claudication? A systematic review. Eur J Vasc Endovasc Surg 2013;46:132-41. 36. Nicolai SP, Viechtbauer W, Kruidenier LM, Candel MJ, Prins MH, Teijink JA. Reliability of treadmill testing in peripheral arterial disease: a meta-regression analysis. J Vasc Surg 2009;50:322-9. 37. Frans FA, Zagers MB, Jens S, Bipat S, Reekers JA, Koelemay MJ. The relationship of walking distances estimated by the patient, on the corridor and on a treadmill, and the Walking Impairment Questionnaire in intermittent claudication. J Vasc Surg 2013;57:720-7.e1. Submitted Sep 10, 2015; accepted Oct 13, 2015.
Intermittent claudication (IC) is the most common symptom of peripheral artery disease (PAD) and results from atherosclerosis in large peripheral arteries.1 Among conservative therapies, supervised exercise therapy (SET) was found to be the most effective tool in decreasing IC symptoms and increasing physical exercise capacity (PEC).2 The term PEC actually reflects what a person is From the Department of Vascular Surgery, Catharina Hospital, Eindhovena; the Department of Epidemiology, CAPHRI School for Public Health and Primary Care, University Maastricht, Maastrichtb; the Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdamc; the Department of Vascular surgery, Maxima Medical Center Veldhoven, Veldhovend; and the CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht.e Author conflict of interest: R.C.v.L. is owner of McRoberts BV, Den Haag, The Netherlands, distributor of the DynaPort MoveMonitor. Correspondence: Joep A. W. Teijink, MD, PhD, Department of Vascular Surgery, Catharina Hospital Eindhoven, PO Box 1350, Eindhoven 5602 ZA, The Netherlands (e-mail: [email protected]). The editors and reviewers of this article have no relevant financial relationships to disclose per the JVS policy that requires reviewers to decline review of any manuscript for which they may have a conflict of interest. 0741-5214 Copyright Ó 2016 by the Society for Vascular Surgery. Published by Elsevier Inc. http://dx.doi.org/10.1016/j.jvs.2015.10.060
maximally capable of in a controlled laboratory setting.3 In patients with IC, PEC is usually determined by a standardized treadmill test (TT).4 With this test, a physician is able to evaluate a patient’s individual walking capacity (ie, maximum walking distance), which is especially useful to determine the effect of SET.4 TT-based walking capacity is frequently used to assess the effect of novel treatment strategies in clinical trials. A recent study demonstrated that an improvement in PEC does not automatically lead to an increase in daily physical activity (PA).5 These insights are in line with a recent study that also found no increase in daily PA levels after SET in patients with IC.6 These results may have important implications because the daily PA level is a strong predictor of morbidity and mortality in patients with IC.7 Several studies have now demonstrated that patients with IC exhibit lower daily PA levels than healthy controls8,9 and that most do not meet the internationally recommended standard for PA.8 The World Health Organisation defines PA as “any bodily movement produced by skeletal muscles that requires energy expenditure.”10 The different aspects of PA include mode, frequency, duration, intensity, and context in which the activities occur. Hence, daily PA reflects the total of actively produced movements a person actually performs daily. 1
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In current practice, PA levels are most commonly determined by interviews or questionnaires. These self-report tools proved to be poorly reproducible11 and seem insensitive for assessing IC symptoms.12 Despite the current possibility to objectively measure PA with the use of activity monitors (ie, pedometers and accelerometers), this approach is rarely applied in daily clinical practice although such devices are small, easily wearable, and found to provide reliable information on PA.13 Patients only need to wear the device for multiple days to obtain an accurate measure of daily PA levels, because PA can fluctuate over days.14 For both research and clinical purposes, it might be important to focus more on daily PA as an outcome parameter.7,15 Whether PEC outcomes, however, could serve as a surrogate marker for PA is currently unknown in patients with IC because the relation between PEC and daily PA is still unclear. For chronic obstructive pulmonary disease (COPD), research has showed that PEC and daily PA are two completely different entities.16 These results underscored the importance of measuring daily PA to optimize treatment for COPD, which might be the same for IC patients. The current study therefore aims to evaluate how different PEC measures relate to daily PA in a general IC population. In line with previous results obtained from a COPD population, we hypothesized that PEC outcomes are poorly associated with daily PA behavior.
PA. All participants were extensively instructed how to wear the PA device properly. Patients returned to our hospital once for the PEC measurements. Assessment of PEC and PA was scheduled in random order. Four researchers (L.G., I.J., R.G., and D.H.) were responsible for the data collection. Measurements ABIs. According to standardized institutional protocols, ABIs were measured by experienced nurses at the Catharina Hospital Vascular Laboratory. A hand-held 8MHz Doppler probe was used to measure systolic pressures at the brachial artery of both arms, followed by the posterior tibial artery or dorsal pedal artery at the level of both ankles. A baseline measurement was performed at rest. Subsequently, a postexercise ABI was obtained (ie, TT at 3.2 km/h and a 6 slope for a maximum of 6 minutes). The ABI was calculated by dividing the highest obtained ankle pressure by the highest obtained arm pressure. PEC. PEC was determined through a sequence of tests. Patients performed a TT, a short physical performance battery (SPPB), a timed up-and-go (TUG) test, and a 6-minute walk test (6-MWT). d
METHODS All procedures in this study were approved by the Catharina Hospital Medical Ethical Committee, Eindhoven, The Netherlands. Participants Between February and May 2015, patients with symptoms of IC were recruited at the vascular surgery outpatient clinic of the Catharina Hospital and from two physical therapy centers, one in Eindhoven and the other in Veenendaal (The Netherlands). Inclusion criteria were PAD Rutherford stage 1 to 3 and an ankle-brachial index (ABI) <0.90 at rest or a drop of >0.15 after a graded TT, or both, or a duplex or magnetic resonance angiography (MRA) demonstrating a significant arterial stenosis (>50%). Exclusion criteria were serious cardiopulmonary comorbidities (New York Heart Association Functional Classification III-IV), PAD Rutherford stage 4 to 6, previous lower limb amputation, use of walking aids, high probability of nonadherence to the protocol (eg, for instance due to dementia), other comorbidities that might limit the patient’s walking ability (eg, severe arthritis, Parkinson disease, recent trauma to the lower extremities), and wearing the PA device for <5 days because that is the minimum number of days for obtaining accurate data on locomotion bouts.17 Patients who were willing to participate were counseled regarding the study protocol before providing written informed consent. Study protocol A cross-sectional design was used to investigate the relation between PEC outcomes and components of daily
d
d
A standard TT was based on the graded GardnerSkinner protocol.18 Patients began walking on a flat treadmill with a constant speed of 3.2 km/h. The incline was increased by 2% every 2 minutes until a maximum of 10% was reached. The test ends at a maximum of 30 minutes (1600 meters). The functional claudication distance (FCD) and absolute claudication distance (ACD) were recorded. FCD was defined as the distance at which a patient prefers to stop because of claudication symptoms.19 ACD was defined as the maximal walking distance limited by intolerable pain. The SPPB, developed by Guralnik et al,20 is a threetest battery that assesses general lower extremity function. In a 3-meter walking velocity test, patients were asked to walk 3 meters at their usual speed. Participants performed the test twice, and the fastest result was selected. In a repeated chair rise test, patients were instructed to stand up and sit down five times as fast as possible with arms folded across the chest. The time for completing the test was recorded, and results generally reflect leg strength and balance.20 In the standing balance test, patients were asked to maintain balance for 10 seconds in the following three positions: feet together (side-by-side position), heel of one foot next to the big toe of the other foot (semitandem position), and the heel of one foot in front of the toe of the other foot (tandem position). A maximum of 4 points can be scored in each of these three tests, leading to a 12-point maximum score. During a TUG test, patients were instructed to rise from a chair, walk 3 meters, turn around, walk back to sit again, all at their fastest pace. The total time was recorded.
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d
A 6-MWT was based on the standardized Montgomery and Gardner protocol.21 Patients were asked to walk up and down a 20-meter distance for 6 minutes, covering as much distance as possible. Total walking distance and the number of stops due to IC symptoms were registered.
Daily PA. PA was measured using the Dynaport MoveMonitor (MM; McRoberts BV, The Hague, The Netherlands), a triaxial seismic accelerometer. This device has been validated to objectively measure PA in patients with IC.13 The MM consists of a rechargeable battery, USB connection, and raw data storage capability on a microsecure digital card. Participants wore the MM on a belt around the waist for 7 consecutive days, except when showering or bathing. The MM includes three orthogonal accelerometers that measure acceleration in three axesdx (longitudinal), y (mediolateral), and z (anterior-posterior)dat a sample rate of 100 samples per second. Raw data were processed using pattern recognition algorithms (MM 1.0.7.21 analysis software) and expressed in terms of body posture (ie, lying and sitting), locomotion, and movement parameters (ie, movement duration, intensity, and frequency). The PA categories retrieved were lying, sitting, standing, shuffling, and locomotion. Shuffling separation divides the active (not walking) parts into two categories: shuffling and transitions. Shuffling is defined as all movement from point A to point B that is not walking. Thus, if the number of steps is fewer than three, or the intensity and direction of the motion do not comply with the characteristics of walking, the movements are classified as shuffling.13 Four subsequent parameters were reported for each activity category: total duration, number of periods, mean duration per period and the weighted mean movement intensity (MI) per period. Data analysis Continuous variables are expressed as means 6 standard deviation when normally distributed and as medians with interquartile ranges (IQRs) in the case of a skewed distribution. Categoric variables are presented with percentages. Normality checks were performed visually using histograms and with a Kolmogorov-Smirnov test. The relation between daily PA (variables being lying, sitting, standing, shuffling and locomotion) and PEC (variables being the outcomes measures of a TT, SPPB, TUG, and 6MWT) was determined by calculating Spearman correlation coefficients (r) because PEC data revealed a skewed distribution. According to Zou et al,22 correlations of $0.50 and $0.80 were considered as, respectively, moderate and strong associations. Multivariate regression analysis was conducted to study the potential influence of gender and age on the relation between PEC and PA. Data were analyzed using SPSS 21.0 software (IBM, Armonk, NY, USA). A P value of <.05 was regarded as being statistically significant. Sample size calculation (with an a ¼ .05 and a
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b ¼ .80) revealed a sample size of 46 participants was needed to realize statistical significant correlations of $0.4 between PEC and PA. RESULTS Recruitment and response. A total of 55 patients met eligibility criteria and were willing to participate. Inclusion of nine patients was based on duplex or MRA images. Nine patients (w17%) were excluded because the MM was worn <5 days (n ¼ 2), technical failure (n ¼ 1), withdrawal due to personal reasons (n ¼ 4), and unanticipated operative interventions during the MM measurement period (n ¼ 2). Therefore, complete data sets of 46 patients were available for analysis. Sample characteristics. Baseline characteristics are summarized in Table I. Mean age was 66 6 9 years, 35 participants (76%) were men, and 21 (46%) demonstrated bilateral complaints. The lowest mean ABI at rest was 0.63 6 0.17, dropping to 0.38 6 0.19 after exercising. Cardiovascular risk factors (smoking, diabetes mellitus) were commonly observed (Table I). PEC. The median FCD and ACD on the TT were 360 meters (IQR, 160.00-536.50 meters) and 438 meters (IQR, 298.25-714.50 meters), respectively. Six patients (13%) achieved the maximum walking distance of 1600 meters. The median walking distance assessed by a 6-MWT was 400 meters (IQR, 320.50-468.50 meters). For the TUG test, a median of 7.58 seconds for completion was found. A total of 87% patients attained a SPPB total score of 10 of 12 (range, 8-12). The SPPB subtests revealed that 76% had a maximum score for balance, 98% had a maximum score for walking speed, but just 18% scored maximal points on the repeated chair rise test (Table II). PA in IC patients. Patients were physically active for 4.46 hours per day (standing, shuffling, locomotion), equivalent to 19% of a 24-hour day. The mean number of hours that patients spent in a sedentary mode (lying or sitting) was 18.98, corresponding with the remaining 81% of a day. Note that this also includes sleeping overnight. Patients initiated a median number of 408 (IQR, 264-611) locomotion periods per day, with a mean MI of 0.187 6 0.022 m/s2 and a median time of 9.88 seconds (IQR, 8.81-12.02 seconds) per period. The median number of transitions from sitting to standing was 52 (IQR, 40-69). A detailed overview of the median daily PA levels is summarized in Table III. Correlation between PEC and daily PA. No significant or relevant correlations (r $ 0.4) were present between SPPB/TUG test outcomes and any of the PA components (data not shown), except for a weak inverse correlation (r ¼ 0.492; P < .01) between locomotion MI and the results of the TUG test. ACD (using a TT) and duration of locomotion per day were moderately correlated (r ¼ 0.561; P < .01). In contrast, corridor-based walking distance (using a 6MWT) and duration of locomotion per day showed just a weak correlation (r ¼ 0.375; P < .01). Stronger correlations were present between total number of steps per day
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Table I. Baseline characteristics of the study population Characteristics Male gender Age, years Body mass index, kg/m2 Affected side Left Right Both Lowest ABIa Rest After exercising Comorbidity Pulmonary Cardiac TIA/stroke Diabetes mellitus Hypertension Current smokers
No. (%) or mean 6 SD (N ¼ 46) 35 (76) 66 6 9 25.1 6 4.9 16 (35) 9 (20) 21 (45) 0.63 6 0.17 0.38 6 0.19 7 10 5 10 32 14
(15) (22) (11) (22) (70) (30)
ABI, Ankle-brachial index; SD, standard deviation; TIA, transient ischemic attack. a Based on ABI values of 37 patients with intermittent claudication (IC).
Table II. Physical exercise capacity (PEC) of the study population Exercise assessment TT FCD, meters ACD, meters 6-MWT distance, meters TUG, seconds SPPB Balance (1-4) Velocity (1-4) Chair rise test (1-4) Total score (1-12)
Median (IQR) 360 438 400 7.58 4 4 3 11
(160-537) (298-715) (321-469) (6.66-9.86) (3-4) (4-4) (2-3) (10-12)
6-MWT, 6-Minute walk test; ACD, absolute claudication distance; FCD, functional claudication distance; IQR, interquartile range; SPPB, short physical performance battery; TT, treadmill test; TUG, timed up and go test.
and TT outcomes (FCD: r ¼ 0.663; ACD: r ¼ 0.621; P < .01 for both). The outcome of the 6-MWT was moderately associated with locomotion MI (r ¼ 0.599; P < .01). The sedentary activity modes did not show any relevant correlations with TT or 6-MWT outcomes (Table IV). Multivariate regression analysis did not reveal any significant contribution of gender or age to the PEC and PA correlations. DISCUSSION In this study we investigated the relation between PEC outcomes and daily PA in patients with IC. Overall, only weak to moderate correlations between these parameters were found. A substantial portion of the correlations was therefore considered of minor relevance (r # 0.5), despite their statistical significance.22 Among relevant correlations, results of a TT (ie, FCD and ACD) showed strongest association with locomotion (ie, total amount of locomotion
and duration of locomotion per period). These findings possibly imply that “the better a TT performance, the more a patient walks in daily life.” Similar conclusions may be drawn from the correlations between TT-based FCD and ACD and number of steps per day. A moderate correlation was also found between a 6-MWT distance and locomotion MI. MI as determined by the acceleration and declaration signals indicates movement power. A comparison between corridor-based walking and treadmill walking demonstrated reduced anterior-posterior ground reaction forces during the latter protocol.23 Therefore, the results of MI and 6-MWT may be expected to demonstrate stronger correlations compared with MI and TT outcomes. Other authors have also reported that 6-MWT results correlated well with PA levels as expressed in general “activity units.”24 Correlations of FCD and PA were generally stronger then the correlations between ACD and PA. It may therefore be proposed that a measurement of FCD is currently the most optimal predictor of daily walking activity. Understanding of the underlying motives of PA behavior is an aid for optimizing intervention strategies.25 This assumption may particularly be true in sedentary populations including IC patients8 because the health benefits of increased PA are likely substantial. However, PA is a complex end point that is influenced by a range of factors (eg, personal, exercise-related, environment, and political decisions).25 Moreover, there is still uncertainty about the exact factors defining someone’s behavior to be physically active. A poor relation between PEC and PA, as shown in this study, may be not surprising because it was also demonstrated in other populations that included healthy elderly individuals14 and COPD patients.16 The concepts of PEC and PA may therefore be different.26,27 Our results indicate that the parameter PEC is able to predict 44% of the variance in PA (FCD vs numbers of steps per day) at best, leaving more than half for other factors to explain PA behaviour. Considering these results and knowing that a low PA level is a strong risk factor for developing comorbidities,7 assessment of PA as an additional outcome measure may have important implications for IC care. Correct assessment of PA in patients with IC seems to be a challenge. Questionnaires may help to identify a severely inactive patient, but these tools largely fail to provide a correct reflection of an entire patient population. Moreover, self-reported responses on daily PA are subject to social desirability bias.28 In contrast, activity monitors were found to be reliable tools for PA assessment and are currently widely available.13 These devices are easy to wear, and the number of problems that were encountered in the present study was minimal. A unique aspect of our methodology was the quantification of a range of different activity components (eg, sitting and standing) rather than more general measures of PA (eg, activity units or metabolic equivalents of a task).24,29 A precise measurement of PA is important because recent insights revealed that prolonged sitting (ie, sedentary behavior)
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Table III. Physical activity (PA) outcomes of the study population Duration hours per day, mean 6 SD
Outcomes Lying Sitting Standing Shuffling Locomotion
10.1 8.52 2.67 0.40 1.30
Activity time per day, mean 6 SD, hoursc Sedentary time per day, mean 6 SD, hoursc,d No. of sit-to-stand, median (IQR) No. of steps per day, median (IQR) MM not worn per day, mean 6 SD, minutesc
6 6 6 6 6
No. of periods per day, median (IQR)
1.92 1.87 1.19 0.26 0.72
9 109 756 409 408
(6-15) (90-141) (528-1202) (277-686) (264-611)
Time per period, seconds or minutes, mean (IQR) 60.8 (41.6-91.6)a 4.38 (3.0-6.1)a 11.02 (9.4-15.9)b 2.77 (2.6-3.0)b 9.88 (8.8-12.0)b
MI, m/s2, mean 6 SD 0.005 0.023 0.052 0.139 0.187
6 6 6 6 6
0.002 0.013 0.012 0.028 0.022
4.46 6 2.0 18.98 6 1.9 52 (40-69) 6315 (3364-9207) 36.9 6 62.1
IQR, Interquartile range; MI, movement intensity; MM, Dynaport MoveMonitor; SD, standard deviation. a Minutes. b Seconds. c Add up to 100%. d Note that the sedentary time also includes sleeping overnight.
Table IV. Spearman correlation coefficients between physical exercise capacity (PEC) and physical activity (PA) TT Variable Lying Duration No of periods Time/period Intensity Sitting Duration No. of periods Time per period Intensity Standing Duration No. of periods Time per period Intensity Shuffling Duration No. of periods Time per period Intensity Locomotion Duration No. of periods Time per period Intensity Steps per day Total No.
FCD, r
ACD, r
6-MWT distance, r
0.173 0.297a 0.306a 0.230
0.138 0.245 0.257 0.190
0.234 0.137 0.114 0.029
0.163 0.305 0.116 0.103
0.196 0.153 0.183 0.025
0.060 0.303a 0.204 0.157
0.253 0.439b 0.459b 0.251
0.281 0.422b 0.387b 0.286
0.228 0.343a 0.245 0.359a
0.449b 0.431b 0.088 0.094
0.438b 0.417b 0.093 0.168
0.406b 0.356a 0.256 0.237
0.617b 0.463b 0.596b 0.277
0.561b 0.422b 0.549a 0.323a
0.375a 0.245 0.499a 0.599b
0.663b
0.621b
0.425b
6-MWT, 6-Minute walk test; ACD, absolute claudication distance; FCD, functional claudication distance; TT, treadmill test. a Statistically significant at P < .05. b Statistically significant at P < .01.
predicts all-cause mortality, independent of these overall PA levels expressed in metabolic equivalents of a task (ie, in MET).30 Prolonged sitting disrupts metabolic functions, resulting in increased plasma triglyceride levels,
decreased levels of high-density lipoprotein cholesterol, and decreased insulin sensitivity.30 Results from molecular biology and medical chemistry studies show that PA and sedentary behavior are thought to have different influences on the body, supporting their independent effects on health.31 Consequently, the positive effect of (vigorous) PA a couple of times per week (for instance during SET) does not seem to compensate for the independent negative effect when one is predominantly sedentary for the remaining time. Thus, incorporating different activity components of PA in patients with IC, in particular, time spent in sedentary modes, may be more predictive compared with measurements of just energy consumption. Previous studies failed to demonstrate beneficial effects of SET on daily PA in patients with IC. Fokkenrood et al6 and Crowther et al32 both concluded that PA levels did not increase after 3 and 12 months of SET, respectively. The finding that an exercise program primarily focusing on stimulating physical capacity does not improve daily PA is explainable. When PAD gradually progresses over the years, the patient likely develops a sedentary lifestyle requiring more than just an increase in exercise capacity to change. This explanation is in line with a previous conclusion from a COPD study.5 Exercise interventions require targeting to exercise capacity as well as to behavior change with regard to daily PA to achieve improvement in PA.5 Techniques to change behavior, such as motivational interviewing, might be helpful in achieving and maintaining improved PA levels. A brief psychologic intervention of just two 1-hour sessions significantly increased daily walking in patients with IC,33 an effect that was sustained for >2 years.34 SET provides an ideal opportunity for incorporation of such psychologic interventions (ie, health behavior-changing techniques), thereby probably optimizing SET programs and improving patient outcome. So, high-quality trials examining the effectiveness of these health behavior-changing techniques in patients with IC are urgently needed.35 The additional value of health
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technology applications (eg, integrated in smart phones) for enhancing PA also requires detailed studies. Based on present results, one may argue that measurement of PEC does not have an additional role in an improved understanding of daily PA in patients with IC. However, such a functional assessment was found to reflect the IC patients’ abilities and limitations36 and may therefore provide valuable information. Walking distance, as measured using a TT, evidently has limitations, because discrepancies between treadmill measures and perceived walking impairment are reported.37 But, it must also be appreciated that treadmill-based walking distance is the most widely used outcome parameter to evaluate treatment effects of SET in clinical trials and daily practice.36 The TUG and SPPB measures may both be of less relevance or suitable for this patient population. The high number of maximum scores (“ceiling effect”) implies that patients were not really challenged during these tests. Both protocols require just a very short period of activity (usually <15 seconds), whereas most IC patients experience pain at a later time. PEC, considered to determine someone’s maximal capacity, may therefore be inadequately estimated by these tests, as also indicated by the lack of correlations with PA. Consequently, we propose to incorporate PA as a novel outcome measure and treatment goal for IC alongside current walking (TT or 6-MWT) PEC measurements. The present study must be interpreted in view of the fact that PEC results may be overestimated because people are generally thought to push to their limits when challenged during a test setting compared with a normal dayto-day situation. It may therefore be possible that this has influenced correlation strength. Moreover, several factors apart from age and gender might influence potential correlations between PEC and PA. Variance in the use of medication, presence of comorbidities, and body mass index, for instance, have a possible effect on PA, and correlations with PEC might be different for certain subgroups of patients with IC. Unfortunately, the study’s cross-sectional design did not allow for studying causality. Future research should preferably explore determinants which possible contribute to PA. Such an analysis may allow for a more selective use of monitoring equipment. CONCLUSIONS Walking distance as determined with a 6-MWT or graded TT protocol revealed weak to moderate correlations with locomotion components of PA, whereas no to weak correlations were found with sedentary PA components. On the basis of the present study results, the sedentary nature of IC patients, and the increased mortality risks accompanied with these low PA levels, we suggest including objectively measured PA as an additional outcome measure and treatment target in patients with IC. We thank Dirk van Dooren for his support in the data analysis and also greatly appreciate the help of Natasja Huls (physical therapist at Fysiotherapie De Jong, Eindhoven, The Netherlands) and Sander van de Hoef (Paramedisch
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