With reimbursement becoming increasingly stringent, it is more important to objectively document outcome measures that clearly demonstrate whether or not an intervention is effective on a case by case basis. This is especially true because the term “potential” is included in the definition of the functional ambulatory levels of the lower limb amputee.1 The rehabilitation team must determine whether a given lower limb component requires less effort to use, improves the net health outcome, and is ultimately as beneficial as or superior to established alternative componentry. Relative to workload, metabolic effort, and oxygen consumption, several methods are available to document change, including scales of perceived exertion, time (or distance) based walking tests, telemetric breath by breath expiratory gas analysis, and others. The challenge is selecting a measurement technique that combines low financial and time commitment with a valid and reliable portrait of actual change in the patient.
Highly technical methods of measuring oxygen consumption and workload are often impractical for routine clinical evaluation because of the high purchase cost and maintenance needs of the equipment. Therefore, if clinicians are expected to objectively document change, less technical and more efficient measures should be considered. This article describes a case, where heart rate (HR) was measured along with a pre/postintervention walking test as a household ambulator transitioned from a nonmicroprocessor knee (Total Knee 2000, Ossur, Reykjavik, Iceland) to a microprocessor knee (C-Leg, Otto Bock; Minneapolis, MN).
An 82-year-old man with left knee discomfort secondary to osteoarthritis underwent partial knee resurfacing. Infections over the course of the following 17 months resulted in a transfemoral amputation. Comorbidities included hypertension and deconditioning. In-patient preprosthetic physical therapy (PT) began immediately after amputation and continued until he was discharged to home 5 weeks after amputation. At that time he was fit with his first preparatory prosthesis. The prosthesis consisted of a flexible interface/rigid frame socket suspended via 3-mm silicone liner with a ratchet type pin lock. The prosthesis included an Ossur, fluid-friction Total Knee 2000, and Ossur K-2 Sensation foot with no cosmetic covering. The prosthetist delivered preliminary instruction on gait, donning, and doffing. An outpatient PT program was initiated for comprehensive gait and balance training. PT with the preparatory prosthesis occurred for the next 6 weeks until he was deemed to be independent with limited community ambulation while using a rolling walker.
Five months after amputation, the patient suffered a fall resulting in a groin injury. This resulted in a temporary decrease in motivation leading to decreased activity and weight gain, necessitating an interface change. During the fitting process, the following gait deviations were noted: short sound side step length and terminal impact. At this point, a third PT program was initiated.
The patient inquired about his candidacy for an Otto Bock C-Leg. As a prerequisite to justifying the need for and benefit from a C-Leg, the patient was asked to perform a baseline walking test using his existing prosthesis. The test was a 75 m timed fastest possible walking speed test on flat concrete with one turn around. A Polar HR monitor (Polar, Irvine, CA) was used to record HR at rest and immediately after the walking test.
The prosthetic clinic had an established loaner C-Leg program in which potential candidates could try the C-Leg for 90 days. The patient was fit into a C-Leg 9 months after amputation. The rehabilitation team initiated a fourth PT program (Table 1). This program was administered 2 days per week for 8 weeks. At the end of the 90-day C-Leg trial period, the 75 m walking test was readministered and compared with baseline. During this course of rehabilitation (1 year) the patient’s antihypertensive medication remained constant.
The timed walking test and measures of HR (Table 2) were useful additions to the clinical notes and for tracking progress in this patient. In addition to the traditional clinical information exchanged between prosthetist, physical therapist, and physician, these data were distributed. In this case, the measures were used to assist in substantiating this patient’s need for and improvement with the component and PT. On discharge from PT with the C-Leg, the patient was again deemed independent with limited community ambulation. Terminal impact was resolved. However, an intractable hip flexion contracture prevented resolution of the unequal step length. Of utmost importance to the patient was the ability to care for his dependent wife and his ultimate goal for participating in rehabilitation. To accomplish this, the patient needed to stand long enough for meal preparation and have the ability to be unrestricted with household mobility in case of an emergent home event. One year follow up with this individual revealed no further falls and successful continued care of the spouse in the home.
Evidence-based practice has been defined as “The conscientious, explicit, and judicious use of current evidence in making decisions about the care of individual patients.”2 Documenting patient outcomes with evidence-based assessment tools is a common practice in medicine and PT. In the United States, this has a direct role in reimbursement. For example, Blue Cross Blue Shield (BCBS), Kaiser Permanente, and the Centers for Medicare and Medicaid Services look to a Technology Evaluation Center (TEC) for evidence-based medical technology assessments.3
The BCBS Association uses the TEC criteria to assess whether a technology improves health outcomes, such as length of life, quality of life, and functional ability. The technology in question must improve the net health outcome and be as beneficial as or superior to established alternatives. Furthermore, the improvement must be attainable outside of the research setting. Most of the TEC criteria have the potential to influence prosthetic practice. Prosthetic practitioners will soon be expected to document patient outcomes using evidence-based instruments as well.
In the management of the patient in this case report, the technology (C-Leg) was more beneficial than his previously established alternative, nonmicroprocessor knee. This would be difficult to substantiate if some evidence-based measure was not performed. Again, we chose a low tech, low cost, and low time intensive assessment of HR and a distance-based walking test. In addition, the physiologic cost index (PCI)4 was calculated in conjunction with target HR (THR)5 as indicators of metabolic effort.
HR has been compared with the gold standards of doubly-labeled water and whole-body indirect calorimetry to determine its ability to accurately measure energy expenditure. Studies have demonstrated that while HR shows different estimates of energy expenditure than doubly-labeled water and whole-body indirect calorimetry, these differences are not statistically significant.6,7 It has been further demonstrated that portable armband measurement of HR also has reasonable concordance with that of doubly-labeled water in measuring total daily energy expenditure.8–10 Therefore, HR can be used to accurately measure energy expenditure. HR is usually used as a guide to set activity intensity because of the relatively linear relationship between it and VO2max.11 HR has also been used in assessing the change in workload caused by using prosthetic and orthotic devices.12–16 However, the validity of using HR as an indicator of energy expenditure can be effected (or limited) by many factors such as anxiety, fatigue, diet, depressants, or stimulants (i.e., caffeine).
For relatively untrained individuals, several weeks of regular exercise training often decreases resting HR and the HR response to a given submaximal exercise workload.11,17 This patient was no exception to this finding. He underwent four separate short PT programs: three preceded the first walking test and the fourth program (Table 1) was completed between the two walking tests. These programs represented this patient’s formal exercise training. It is also possible that confidence had a role in enabling this patient to become more ambulatory. The sources of confidence in this case could have come from the perception of receiving a superior component or therapy; or confidence may have actually been enhanced by supervised gait and balance training, experience ambulating as an amputee, and/or the introduction of the C-Leg.18–20
Several walking tests have been used clinically and for research. When attempting to minimize equipment and expense, generally speaking, one must first choose between time-based or distance-based walking tests.20–25 There are several considerations when choosing between these tests. For instance, one can blind the person from how much longer they would need to walk by choosing the time-based test. Conversely, a distance based-test can be used to motivate the patient to work hard through the finish by enabling them to see the end point of the test.
Regardless of which walking test is selected, critical time/distance information is attained enabling the calculation of velocity. Velocity data are vital information to have when pursuing reimbursement for highly technical, newly released componentry that carries an experimental label or that is guarded by functional levels distinguished by terms such as “variable cadence.” These data additionally inform the practitioner or researcher about differences in performance between previous or later trials, across differing components, therapy sessions, or simply time.
By having both velocity and heart rate data, calculation of the PCI is possible. The PCI is based on the established relationship between HR and VO2. It is calculated by subtracting the resting HR from the walking HR then dividing by the walking velocity. The measure results in units of beats per meter.4,26,27
We calculated the PCI as an additional measure to confirm our finding of reduced HR, suggestive of decreased workload after use of the C-Leg and therapy. The PCI seemed to diminish the difference. Ijzerman and Nene reported that subtracting the baseline (or resting) HR during calculation was probably not helpful. We found this to be true because the reduction of the working HR was mathematically similar to the reduction observed in resting HR. This caused the resulting quotients to be similar, diminishing the effect size.
As a final consideration of low tech evaluative methods of workload, we calculated the THR (Table 2). It should be noted that selection of THR and workload intensity is to be decided based on specific attributes and needs of the patient. Common criteria indicate that deconditioned persons would probably be best served by beginning an exercise regime with a workload of 40% to 50% THR. More conditioned individuals may work in a THR range as high as 85% to 90%.11 A typical training intensity for most individuals would be set at 60% of the HR reserve.28
This patient’s theoretic maximum HR (220 − age) was 138 bpm. The baseline 75 m walking test was administered 9 months status-posttransfemoral amputation. This followed approximately 8 months of using the nonmicroprocessor knee and three PT programs. The postexercise HR at this test was 140 bpm. This is 101.5% of his theoretic maximum HR. The patient’s heart was beating at a rate well in excess of a THR calculated at 45%. The second 75 m walking test was administered at approximately 1 year status-postamputation. This followed the fourth PT program and 90 days of use of the C-Leg knee prosthesis. Here, the patient’s postexercise HR was 112 bpm or 81% of his theoretic maximum HR. More importantly, the latter postexercise HR was in closer agreement with the 60% THR calculation suggestive of a training effect. A training effect could offer explanation for some of the observed bradycardia during exercise; however, it is well established that a 12–15 bpm reduction of HR during exercise can be expected after a longer exercise program.17 Therefore, it is possible that the drastic difference could additionally be explained by the patient’s inability or unwillingness to perform a maximal effort.29
Orendurff et al. found that individuals using the C-Leg tend to walk faster, but metabolic gait efficiency was not significantly improved. The patient did not increase his gait velocity but did demonstrate reduced exercise and resting HRs, potentially explained by a training effect. We feel that one possible explanation for the difference is in the type of person studied. Many investigators have used convenience samples of subjects who can physically and independently attend an outpatient facility, which suggests a community level of ambulation.18–20 In addition, many investigators attempt to control the type of component being compared as opposed to controlling for the functional level of the individuals being studied. Either method has its advantages. However, in this case, we believe that offering a deconditioned, elderly patient, that was goal oriented and generally motivated, the opportunity to attend PT and use a component that offers technological improvements in areas of function that are physiologically deficient, cumulatively explain the differing results.
This individual’s goals were met because of improved prosthetic technology and multidisciplinary rehabilitation. Although difficult to quantify, confidence, motivation, and perception of intervention were possible factors contributing to the outcomes. We recommend conducting simple, low cost, minimally technical, and time intensive measures such as HR, THR calculation, and walking tests as an adjunct to clinical prosthetic patient management. We did not find the PCI calculation to be beneficial as it actually minimized the difference in performances relative to simpler HR, time, and velocity metrics. This case highlights the importance of incorporating objective, evidence-based measures in the justification for reimbursement of prosthetic componentry. These simple measures are also useful for tracking patient progress between components, through PT treatment and simply over time.
The authors thank Westcoast Brace and Limb for the time and facilities necessary to conduct this work.
1. Centers for Medicare and Medicaid Services. U.S. Department of Health and Human Services. Healthcare Common Procedure Coding System. Springfield, VA: U.S. Department of Commerce, National Technical Information Service; 2007.
2. Sackett DL, Straus S, Richardson S, et al. Evidence-Based Medicine: How to Practice and Teach EBM
. 2nd ed. London: Churchill Livingston; 2000.
3. Technology Evaluation Center
. Blue Cross Blue Shield Association. 2008. Available at: http://www.bcbs.com/betterknowledge/tec/tec-criteria.html
4. Boyd R, Fatone S, Rodda J, et al. High- or low- technology measurements of energy expenditure in clinical gait analysis. Dev Med Child Neurol
5. Goldberg L, Elliot DL, Kuehl KS. Assessment of exercise intensity formulas by use of ventilatory threshold. Chest
6. Davidson L, McNeill G, Haggarty P, et al. Free-living energy expenditure of adult men assessed by continuous heart-rate monitoring and doubly-labelled water. Br J Nutr
7. Garet M, Boudet G, Montaurier C, et al. Estimating relative physical workload using heart rate
monitoring: a validation by whole-body indirect calorimetry. Eur J Appl Physiol
8. Morio B, Ritz P, Verdier E, et al. Critical evaluation of the factorial and heart-rate recording methods for the determination of energy expenditure of free-living elderly people. Br J Nutr
9. Wareham NJ, Hennings SJ, Prentice AM, Day NE. Feasibility of heart-rate monitoring to estimate total level and pattern of energy expenditure in a population-based epidemiological study: the Ely young cohort feasibility study 1994–5. Br J Nutr
10. St-Onge M, Mignault D, Allison DB, Rabasa-Lhoret R. Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr
11. American College of Sports Medicine. American College of Sports Medicine’s Guidelines for Exercise Testing and Prescription
. 6th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2000.
12. Stallard J, Rose GK. Clinical decision making with the aid of ambulatory monitoring of heart rate
. Prosthet Orthot Int
13. Stallard J, Rose GK, Tait JH, Davies JB. Assessment of orthoses by means of speed and heart rate
. J Med Eng Technol
14. Davies JB. Use of heart rate
in assessment of orthoses. Physiotherapy
15. Schmalz T, Blumentritt S, Jarasch R. Energy expenditure and biomechanical characteristics of lower limb amputee
gait: the influence of prosthetic alignment and different prosthetic components. Gait Posture
16. Gailey RS, Lawrence D, Burditt C, et al. The CAT-CAM socket and quadrilateral socket: a comparison of energy cost during ambulation. Prosthet Orthot Int
17. McArdle WD, Katch FI, Katch VL. Exercise Physiology: Energy, Nutrition and Human Performance.
Baltimore, MD: Lippincott, Williams & Wilkins; 2007.
18. Hafner BJ, Willingham LL, Buell MC, et al. Evaluation of function, performance, and preference as transfemoral
amputees transition from mechanical to microprocessor control of the prosthetic knee. Arch Phys Med Rehabil
19. Orendurff MS, Segal AD, Klute GK, et al. Gait efficiency using the C-Leg
. J Rehabil Res Dev
20. Kahle JT, Highsmith MJ, Hubbard S. Comparison of non-microprocessor prosthetic knee mechanisms and the C-Leg
in the PEQ, stumbles and falls, walking tests, stair descent, and knee preference. J Rehabil Res Dev
21. Brooks D, Hunter JP, Parsons J, et al. Reliability of the two-minute walk test in individuals with transtibial amputation. Arch Phys Med Rehabil
22. Montero-Odasso M, Schapira M, Varela C, et al. Gait velocity in senior people. An easy test for detecting mobility impairment in community elderly. J Nutr Health Aging
23. Montero-Odasso M, Schapira M, Soriano ER, et al. Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J Gerontol A Biol Sci Med Sci
24. Horemans HL, Beelen A, Nollet F, Lankhorst GJ. Reproducibility of walking at self-preferred and maximal speed in patients with postpoliomyelitis syndrome. Arch Phys Med Rehabil
25. Simonsick EM, Montgomery PS, Newman AB, et al. Measuring fitness in healthy older adults: the Health ABC Long Distance Corridor Walk. J Am Geriatr Soc
26. Butler P, Engelbrecht M, Major RE, et al. Physiological cost index of walking for normal children and its use as an indicator of physical handicap. Dev Med Child Neurol
27. Ijzerman MJ, Nene AV. Feasibility of the physiological cost index as an outcome measure for the assessment of energy expenditure during walking. Arch Phys Med Rehabil
28. Swain DP, Abernathy KS, Smith CS, et al. Target heart rates for the development of cardiorespiratory fitness. Med Sci Sports Exerc
29. Gellish RL, Goslin BR, Olson RE, et al. Longitudinal modeling of the relationship between age and maximal heart rate
. Med Sci Sports Exerc