Abstract
Purpose
Using preliminary data, we examined: (i) patterns of body mass index (BMI) over the year following amputation by amputation level and (ii) the association between BMI and mobility and prosthetic device use.
Method
Patients from three medical centers undergoing dysvascular amputation (N = 87; M age = 62) participated in interviews pre-surgically and at 6 weeks, 4 months, and 12 months following amputation. The main outcome was self-reported BMI, adjusting for limb weight lost due to amputation. Additional outcomes were mobility and time spent using and walking in a prosthetic device.
Results
Adjusted BMI slightly decreased at 6 weeks (pre-surgery M = 31.2; 6 weeks M = 30.3) and 4 months (M = 30.7) but exceeded baseline levels by 12 months (M = 31.7). There were no significant BMI differences by amputation level. In multivariable analyses, higher pre-surgical BMI was associated with fewer hours of prosthetic device walking at month 4 (β = −0.49) and poorer overall mobility at month 12 (β = −0.22).
Conclusions
BMI increased at one year following amputation surgery. Higher pre-surgical BMI was associated with poorer mobility and prosthetic device use. Interventions are needed to prevent excess weight gain in the year following amputation.
Keywords: Mobility disability, obesity, prosthesis
Introduction
The prevalence of lower limb amputations in the United States has been estimated at 1.6 million in 2005 and this is projected to double by the year 2050 [1]. The majority of lower limb amputations are due to non-traumatic causes, with 60% of non-traumatic amputations being due to diabetes [2]. The rates of trauma-related and cancer-related amputations have declined while amputations associated with vascular diseases are increasing. There was a 27% increase in dysvascular amputations between 1988 and 1996 [1].
Obesity is associated with poor outcomes across a variety of health conditions. Obesity has been linked to the development and progression of cardiovascular disease and hypertension, to elevated levels of cholesterol, and elevated blood glucose [3,4] as well as to increased mortality [5], and increased risk of disability [6]. As a result, it is considered an important modifiable risk factor in the management of many health conditions.
The relationships between obesity and amputation are complex. Obesity is a risk factor for amputation, amputation may play a role in further weight gain, and over time weight gain post-amputation could be associated with a downward health trajectory. There is cross-sectional evidence suggesting that amputation is related to obesity. In one such study of individuals with traumatic lower limb amputation, Kurdibaylo found that the anatomic level of the amputation was related to obesity, as measured with calipers in 9 body areas [7]. Those with transtibial (37.9%) amputation had the lowest obesity rate followed by those with transfemoral (48%) and bilateral amputation (64.2%). Subcutaneous fat mass was greater among amputees compared to a comparison group of non-amputees. High rates of excess body fat were observed in a second study. Based on waist to hip ratio (WHR > 0.9), over 80% of traumatic bi-lateral lower limb amputees were considered to be obese [8]. Because the data for these studies was cross-sectional, it was not clear whether the excess body fat preceded or followed the amputation.
Weight may play a role in recovery after amputation. For example, obesity may be associated with difficulties in healing an amputation wound and in fitting a prosthesis [7]. One study found that heavier weight was correlated with more prosthetic device repairs [9], although not all studies have found a relationship between BMI and functional outcomes [10]. More generally, individuals with lower extremity amputation already have increased risk of coronary heart disease and stroke [11], and amputees who are obese have been shown to have higher diastolic hypertension and lower HDL cholesterol than nonobese amputees, suggesting that higher weight among amputees compounds an already elevated risk of morbidity.
Most of the studies mentioned relied on samples with traumatic amputation, who make up only 10% of the population with lower limb amputation. Yet, it is important to examine body weight changes among non-traumatic, vascular related amputees due to their higher prevalence, increasing rates in the population [1], and relationship between weight and overall health. Focusing on non-traumatic amputees is further supported by a recent review that found that those undergoing dysvascular amputation have poorer walking outcomes than those due to trauma [12].
Little is known about the course of weight and BMI change after amputation, and the literature exploring the correlates of weight post-amputation is sparse. Body Mass Index (BMI, kg/m2) is a commonly used indicator of excess weight [4]. Better understanding the trajectory of weight gain post-amputation will help inform interventions related to prevention of further disability. The current study was an exploratory investigation to further our understanding of BMI and weight following amputation. Our primary goals were to: (i) examine changes in BMI in the first year post amputation overall and by amputation level (ii) evaluate the association between BMI and mobility and prosthetic device use and (iii) based on our preliminary findings, make recommendations for future studies.
Methods
Study design
Data for the current study were extracted from a larger, multisite prospective cohort study of individuals undergoing major lower limb amputation due to vascular disease and/or diabetes. Participants were recruited from three participating centers (two large medical centers in Seattle, WA and one large medical center in Denver, CO) between September 2005 and December 2008.
Participants
Individuals were screened for inclusion using the following criteria: (i) age 18 years or older, (ii) awaiting (or underwent in the last 6 weeks) a first major amputation, defined as a unilateral primary transmetatarsal (TM), primary transtibial (TT), or primary transfemoral (TF) amputation or revision of a first major unilateral amputation that occurred within the last 6 weeks, and (iii) the primary cause of amputation was diabetes or peripheral vascular disease. Participants were excluded if (i) they had inadequate cognitive or language function to consent or participate defined by ≥6 errors on the Short Portable Mental Status Questionnaire (SPMSQ) or (ii) they were non-ambulatory before the amputation for reasons unrelated to peripheral vascular disease or diabetes. Of the 239 major lower extremity amputations, 136 (57%) met study criteria. Thirteen participants were excluded as a result of being unable to verify eligibility at one facility due to privacy standards. Other reasons for ineligibility included previous contralateral major amputation (38%); dementia, failure of SPMSQ or other disease process causing speech pathology (22%); revision of a previous major amputation >6 weeks prior to enrollment (12%); non-ambulatory (11%); bilateral amputation (10%), no contact information to allow for adequate follow-up (3%), and other reasons (3%). Of the 136 eligible patients, 87 individuals (65%) consented to participate. Institutional review board approval was obtained at each study site. Full details of our sampling and procedures are described elsewhere [13].
Procedure
Twenty-nine of the 87 subjects were enrolled pre-surgically (33%). Due to logistical, medical, and time constraints, the majority of subjects were enrolled six weeks post-surgically (67%). All data were collected on standardized case report forms. Interviews were conducted both in-person and over the telephone by trained study coordinators. Regardless of whether subjects were enrolled pre- or post-surgically, they completed an interview that asked about pre-morbid mobility, pre-surgical (baseline) self-reported weight and height, and a variety of demographic factors and health behaviors prior to amputation (referred to as “pre-surgical” or “baseline”). Follow-up assessments were conducted 6 weeks (for those enrolled pre-surgically only), 4 months, and 12 months following their amputation. Those enrolled 6 weeks post amputation completed both the pre-surgical and first follow-up assessment (6 weeks) at that time. Such recall techniques have been successfully used by our research group [14]. And self-reported weight is used extensively in examinations of other health conditions [15–17].
Measures
Demographic information was collected at the pre-surgical interview. BMI and mobility data were collected at all time points. Amputation related-outcomes, consisting of surgical wound healing, prosthesis fitting, prosthesis use, and walking in prosthesis, were collected at 6 weeks, 4 months, and 12 months post amputation.
Demographic and medical data included age, gender, marital status, race, employment status, education level, income, and information about the index amputation. The anatomic level of amputation, TM, TT, or TF, was determined from the medical record.
Body mass index was calculated using self-reported height and weight because this was a preliminary study without funding to conduct measured height and weight. While self-reported height and weight is not ideal, due to the absence of any investigations on this topic, we deemed this to be acceptable for our preliminary study to see whether any relationships with BMI emerged. In addition, self-reported data are largely found to underestimate BMI which would yield more conservative results [18]. Participants reported their pre-amputation height and weight at the pre-surgical interview. Participants were asked to estimate their weight at the designated time point to the best of their ability without a prosthetic device. The formula used to calculate BMI was: weight (lbs)/[height (in)]2 × 703. We calculated a post-amputation-adjusted body weight to account for the missing limb based on the body proportions suggested by Durkin (2003) [19] and the methods proposed by Osterkamp [20] and Himes [21]. Height was carried forward from baseline. The post-amputation adjusted body weight (WtE) was calculated using the estimating equation: WtE = Wto/(1 − P) where Wto is the self-reported body weight and P is the proportion of total body weight represented by the missing limb. According to Durkin’s calculations for men over 55 years of age, the proportion of total body weight attributed to different body segments was as follows: 1.3% foot, 3.93% shank, 11.83% thigh. Based on expert opinion, we estimated that in a TM amputation, 40% of the foot is removed; in a TT amputation 100% of the foot and 50% of the shank is removed; and in a TF amputation 100% of the foot and shank are removed and 40% of the thigh is removed. Applying these percentages to the proportions estimated by Durkin, the final estimated percent of whole body mass removed during amputation was: 0.52% for a TM amputation, 3.265% for a TT amputation, and 9.962% for a TF amputation.
For women, we used Durkin’s proportions for women over age 55 years which were: 1.19% foot, 4.33% shank, 12.98% thigh. Using the same estimations for the percent of each segment which is removed during amputation, we estimated that the percent of whole body mass removed during amputation for females was: 0.476% for a TM amputation, 3.355% for a TT amputation, and 10.712% for a TF amputation.
To categorize participants’ weight status, a pre-surgical BMI or post-amputation adjusted BMI of <18.5 was considered underweight, 18.5–24.9 was considered normal weight, 25–29.9 was considered overweight, 30 and above was considered obese [4].
Mobility was assessed using the Locomotor Capability Index (LCI-5): 14-items are graded on a 5-level ordinal scale ranging from, “unable to perform the activity” (0 points), to “able to perform independently without assistance” (4 points) [22]. Scores for the LCI-5 range from 0 to 56 points with higher scores representing higher function. Among amputees, the LCI-5 has well-established internal consistency, test-retest reliability, and content, discriminate, and criterion validity. Additionally, it has a lower ceiling and a larger effect size than the original LCI, making it an appropriate tool for detecting functional changes in this population [22]. Although it has not been validated among non-amputees, the responses are sufficiently generic (i.e. not prosthesis- or amputation- specific) that it could be used to assess mobility both pre- and post-amputation. Pre-morbid mobility was defined as the level of mobility just prior to the development of the injury or illness that led to the amputation (e.g. ulcer, edema, associated pain) affecting the extremity undergoing amputation. Participants were asked to complete the LCI-5 based on recall of function “immediately prior to developing any limitations in your leg that was amputated.”
Pre-morbid mobility was assessed six weeks after surgery for all enrolled subjects. For subjects recruited pre-surgically, pre-morbid mobility was also assessed prior to amputation. To assess the test-retest reliability of our retrospective pre-morbid mobility assessment, a comparison was made of the responses at the two time periods for the 29 subjects enrolled preoperatively. The intraclass correlation coefficient of the 6-week post-surgically assessed pre-morbid score (agreement if recall was within 3 points) to the pre-surgical recall of the pre-morbid score was 0.87 (p = 0.003). To further ensure the reliability of the mobility measure the 6-week recall of pre-morbid mobility was compared between those recruited pre-surgically and those recruited post surgically, and the mean difference was only 1.6 points (p = 0.52).
Amputation related outcomes
Survey items developed specifically for this study were administered at 6 weeks, 4 months, and 12 months and asked participants if their surgical wound had healed (yes or no). Participants were then asked if a prosthetic device had been fitted (yes or no). If they responded affirmatively, they were asked to report the average number of hours per day spent wearing it and walking with it. Finally, participants were asked whether they used an ambulation aid (e.g. wheelchair, walker, crutches, or cane).
Data analysis
Patterns of BMI and categories of weight status were described using descriptive statistics. To test for BMI differences by demographic variables and level of amputation, one-way analysis of variance (ANOVA) was used for continuous outcomes (e.g. BMI) and Chi-Square tests were used for categorical outcomes (e.g. BMI category). Multiple linear (for continuous outcomes) and logistic (for dichotomous outcomes) regression models were used to determine whether baseline BMI was associated with mobility outcomes. Due to the small sample size, only demographic variables that were statistically significantly associated with either mobility or an amputation related outcome (p < 0.05) were included as covariates in models. Level of amputation was included a priori in regression models. SPSS version 16.0 was used to conduct all analyses.
Results
Participants were on average 62.1 years of age, the majority (83.9%) were Caucasian and just over half were married (Table I). There was one death following enrollment but prior to data collection and resulted in a sample size of 87 at the pre-surgical interview. Seventy-five individuals completed the 12 month assessment. Reasons for attrition included: withdrawing from the study (N = 4), lost to follow-up (N = 2), and death (N = 6). At the pre-surgical interview, the majority of participants reported living at home alone (30.2%) or at home with a spouse, family, or a friend (60.5%), while a smaller number lived in a retirement community, assisted living, or nursing home (9.3%). There were no significant differences in amputation level or baseline BMI by demographics (data not shown).
Table I.
Baseline demographic data.
| Variable | Total sample (N = 87) |
|---|---|
| Age M (SD) | 62.1 (±8.7) |
| Male n (%) | 80 (92.0) |
| Marital status n (%) | |
| Not married/partnered | 38 (44.2) |
| Married/partnered | 48 (55.8) |
| Race n (%) | |
| Caucasian | 73 (83.9) |
| Black | 9 (10.3) |
| Other | 5 (5.7) |
| Employment status n (%) | |
| Not employed | 77 (89.6) |
| Employed | 9 (10.4) |
| Education level n (%) | |
| High school | 32 (37.2) |
| Some college | 36 (41.9) |
| College degree or higher | 18 (20.9) |
| Income n (%) | |
| ≤25,000 | 38 (45.8) |
| 25,001–50,000 | 32 (38.6) |
| >50,000 | 13 (15.6) |
High school = some high school or high school graduate.
M (SD), mean (standard deviation).
Weight and BMI course
Self-reported mean weight pre-amputation was nearly 220 pounds (see Table II). Examining the patterns for mean weight unadjusted for limb loss, weight declined for the sample overall at 6 weeks and four months and was lower than baseline at 12 months. However, examining mean weight adjusted for limb loss, weight declined at 6 weeks and 4 months but increased by 12 months for an overall gain of just over 5 pounds (see Table II). Average BMI was just over 31 pre-surgically and had increased, based on adjustments to account for limb loss, by 12 months (see Table II). To test whether differences in BMI over time could be explained by attrition, pre-surgical BMI was compared between individuals who did and did not complete follow-up. There was no significant difference between these two groups on pre-surgical BMI.
Table II.
Weight, BMI, mobility, and prosthetic device use patterns following major lower extremity amputation.
| Pre-surgery (N = 86) | 6 weeks (N = 84) | 4 months (N = 80) | 12 months (N = 75) | |
|---|---|---|---|---|
| Unadjusted weighta M (SD) | 219.95 (56.9) | 207.6 (54.2) | 212.4 (57.6) | 218.9 (58.5) |
| Adjusted weightb M (SD) | n/a | 214.3 (56.1) | 219.1 (59.9) | 225.9 (60.9) |
| BMIc M (SD) | 31.2 ± 7.4 | 30.3 (7.3) | 30.7 (7.6) | 31.7 (7.7) |
| BMI category n (%) | ||||
| Underweight | 1 (1.2%) | 1 (1.2) | 1 (1.3) | 1 (1.3) |
| Normal | 15 (17.6%) | 15 (18.3) | 18 (22.5) | 11 (14.7) |
| Overweight | 27 (31.8%) | 34 (41.5) | 26 (32.5) | 25 (33.3) |
| Obese | 42 (49.4%) | 32 (39.0) | 35 (43.8) | 38 (50.7) |
| Mobilityd M (SD) | 48.0 (10.7) | 33.1 (18.3) | 37.9 (16.2) | 40.8 (15.5) |
| Stump healed n (%) | n/a | 46 (52.8) | 57 (71.3) | 68 (90.7) |
| Prosthesis fitted n (%) | n/a | 15 (18.1) | 42 (52.5) | 69 (92.0) |
| Hours wearing prosthesis/day | n/a | 2.63 (2.82) | 7.56 (4.94) | 9.25 (5.47) |
| Hours walking prosthesis/day | n/a | 1.09 (1.05) | 3.34 (3.69) | 4.25 (3.96) |
M (SD), mean (standard deviation).
Self-reported, unadjusted weights.
Adjusted for limb loss using estimations from equations described in the methods section.
BMI using un-estimated weight at baseline and estimated weight at follow-up points.
Mobility was measured with the Locomotor Capabilities Index (maximum score is 56, higher scores represent higher function).
The majority of participants were classified as obese at the pre-surgical interview (49.4%), while 31.8% were classified as overweight. The majority of participants stayed within the same BMI classification over the course of the year following amputation (N = 54; 72%) such that at 12 months 50.7% of the sample was classified as obese and 33.3% were classified as overweight. Six participants moved from the overweight to obese category while 4 moved from normal weight to overweight. Five participants moved from the obese to overweight category while 3 moved from overweight to normal weight. Thus, while the proportion of participants in each category remained fairly constant over time, roughly 12% of the sample moved to a more unhealthy BMI, and roughly 10% moved to a healthier BMI category. Examining BMI by amputation level (see Table III), those with a TF amputation had higher BMIs at the pre-surgical assessment, though this was not significant. At 12 months, there was a trend for those with a TT amputation to have a higher BMI (p = 0.08).
Table III.
BMI by Index Amputation Level
| Amputation level | TM (N = 27) Mean (SD) | TT (N = 51) Mean (SD) | TF (N = 7) Mean (SD) | F | p value |
|---|---|---|---|---|---|
| BMI (baseline) | 29.8 (6.0) | 31.6 (7.8) | 34.0 (9.0) | 1.08 | 0.35 |
| BMI (6 weeks) | 29.6 (6.0) | 30.4 (7.5) | 31.8 (10.7) | 0.24 | 0.79 |
| BMI (4 months) | 29.3 (6.3) | 31.3 (7.6) | 32.8 (11.7) | 0.87 | 0.42 |
| BMI (12 months) | 29.0 (5.8) | 33.4 (7.8) | 32.1 (11.2) | 2.67 | 0.08 |
TF, transfemoral; TM, transmetatarsil; TT, transtibial.
BMI and wound healing, prosthetic device walking, and mobility outcomes
The association between BMI and mobility and prosthetic device walking outcomes at months 4 and 12 was examined with hierarchical linear regression. After controlling for age, gender, and amputation level, higher pre-surgical BMI was associated with poorer mobility (as measured with the LCI) at month 12 (β = −0.22, p < 0.05) with a similar trend at month 4 (β = −0.23, p < 0.07). Similarly, higher pre-surgical BMI was associated with fewer average hours per day walking with a prosthetic device at month 4 (β = −0.49, p < 0.01) but not at month 12 (see Table IV).
Table IV.
Hierarchical multiple regression correlating BMI with mobility and time walking with a prosthetic device at 4 and 12 months.
| Variable | Mobility (month 4) |
Mobility (month 12) |
(walking month 4) |
(walking month 12) |
||||
|---|---|---|---|---|---|---|---|---|
| β | R2cha | β | R2cha | β | R2cha | β | R2cha | |
| Age | −0.20 | 0.04 | −0.22b | 0.18c | −0.16 | 0.03 | −0.15 | 0.05 |
| Gender | −0.12 | −0.32c | 0.23 | −0.09 | ||||
| Level | 0.07 | −0.17 | 0.11 | −0.12 | ||||
| BMI | −0.23a | 0.05a | −0.22b | 0.04b | −0.49c | 0.18c | −0.03 | 0.00 |
| R2tot = 0.09a | R2tot = 0.22b | R2tot = 0.21b | R2tot = 0.05 | |||||
Each group of variables was entered on a separate step in the order specified in the table. Results presented control for demographics entered in Step 1. Gender was coded as male = 0 and female =1. Level = Amputation level (transfemoral vs. all other).
Mobility = Locomotor Capabilities Index. Walking = Self-reported hours walking with a prosthesis per day.
β, standardized regression weights; R2cha, r-squared value for the individual regression step; R2tot total r-squared value for the model; BMI, baseline body mass index.
p < 0.07
p < 0.05
p < 0.01.
The association between BMI and the use of a gait aid was examined with multivariable logistic regression. After controlling for age, gender, and amputation level, higher pre-surgical BMI was associated with an increased likelihood of using a gait aid at month 12 (OR = 1.14; 1.02–1.27). Pre-surgical BMI was not significantly associated with residual limb healing or having a prosthetic device fitted (data not shown).
Discussion
This study examined the weight and BMI patterns of individuals undergoing their first unilateral amputation for dysvascular disease from pre-surgery to one year follow-up. According to absolute weight, BMI dropped during the course of the first year. However, by adjusting weight to account for the body mass lost during amputation, we were able to calculate more accurate BMIs which showed that average BMI values decreased post surgery, but then rebounded and ultimately exceeded baseline values by the time of one year follow-up. At 6 weeks, weight had declined from baseline levels, as expected due to the nature of healing from major surgery. At 4 months weight was similar to baseline levels and by 12 months, the sample had gained approximately 6 pounds over baseline. While the overall weight gain of 6 pounds is not large, if continued over many years significant weight gain would occur putting already at-risk individuals at even higher risk for further complications due to obesity such as heart disease [23]. At the same time, amputation may offer an important motivational opportunity for individuals to engage in weight loss interventions which could help decrease future health risks.
Notably, overall in this sample, we found exceedingly high rates of combined overweight and obesity at baseline (81.2%) and 12 months (84%) compared to the general U.S. adult population (68.0%) [24]. At twelve months, over half of the sample was classified as obese while the Healthy People 2020 objective is 30.6% [25]. The findings suggest that a majority of individuals undergoing amputation are overweight or obese, and the experience of amputation is associated with additional weight gain for a majority of amputees in the first year following limb loss. These findings suggest that amputation is a risk factor for further weight gain in an already overweight/obese population, and there is a need for significant interventions to prevent this additional weight gain post-amputation, perhaps via interventions targeted at increased physical activity, weight loss, and nutrition.
Our findings showed no differences in baseline BMI by initial amputation level, suggesting that obesity was not necessarily associated with higher amputation levels. By one year, individuals with transtibial amputations had higher BMIs on average, but this difference was still not statistically significant, likely due in part to the small sample size in the study. Larger samples can help determine whether this trend is accurate. If it is, those with transtibial amputations may be a particularly important target for weight loss interventions, as they potentially have the greatest potential for mobility. This group had the highest increases in BMI over the 1-year period and are likely at high-risk for amputation revisions or contralateral amputations, yet can still be quite mobile with the use of a prosthetic device.
While there was little relationship between BMI and both stump healing and initial receipt of a prosthetic device, in general, individuals with higher BMI values reported lower mobility and less time walking on their prostheses, though the strength of this association varied over time. These findings suggest that there may be a reciprocal and cumulative interaction between BMI, ambulatory ability, and physical activity. Individuals with higher BMI may be less active in the months following amputation, which could lead to additional weight gain and added difficulty of walking. This relationship is complicated by the fact that weight change may detract from the comfort and fit of a prosthetic device, which may further deter use, which in turn might lead to a spiraling downwards in ambulation and physical activity.
There is evidence that significant health benefits, including reductions in the rates of all-cause mortality, coronary heart disease, stroke, and type 2 diabetes, can occur from developing a physically active lifestyle even among persons who are overweight or obese [26]. In addition, physical activity can result in improved mobility among persons who are obese [27]. Thus, an intervention to promote healthy dietary and physical activity habits while encouraging limitations on sedentary behaviors, such as television watching, even if it does not result in weight loss could benefit the health of individuals with amputation. There is a dearth of research on physical activity interventions in persons with amputations [26]. There are few interventions that promote weight loss among people with obesity related mobility disability but existing research shows some positive outcomes on mobility [6].
Several limitations of this study are worthy of note. Our sample size was modest, and some analyses may have been underpowered to detect small associations. Another limitation is the use of self-reported measures, particularly for height and weight. There is evidence that self-report and actual weight are highly correlated though older age groups (over 60) have been found to under-report weight [28]. A recent review found that, in the vast majority of studies, BMI is underestimated when using self-reports [18]. If the latter occurred, there is a potential that our results are conservative. However, we feel the importance of the topic and dearth of research examining BMI trends in persons with amputation justifies our preliminary investigation using self-reported data. More research examining the validity of self-reported height and weight among people with amputations would be beneficial. As this was a secondary analysis of an existing data set this type of question could not be addressed. In addition, participants were not specifically queried about the date of their most recent weighing. All participants were receiving comprehensive amputation care. Research shows that Veterans with diabetes have 4.6 primary care visits per year [29] and standard practice includes taking weights. Additionally, waist circumference may be a better indicator of adiposity in those with lower limb loss due to relationships between central adiposity and adverse health outcomes. Another limitation is that we did not directly measure physical activity, rather self-reported time spent walking and wearing a prosthetic device were used as proxy variables. Future studies would benefit from the use of objective measures of physical activity in this population [30]. Also, while it is strength that we adjusted weight for amputation so that BMI could accurately be calculated, we estimated the proportion of weight attributed to amputation without taking into account the precise point of amputation. However, by applying the same proportions across the sample, error should be minimized. Future studies should measure height and weight in populations with amputation. Weight should be assessed with the prosthesis and without so the exact body weight without a prosthetic device is known. Finally, the present findings may not generalize to less healthy individuals with limb loss, as only 57% of those screened were eligible to participate. Rates of complication (i.e. revision to a higher level, contralateral amputation, and mortality) are elevated in this population, and the primary reason for exclusion from this study was already having experienced a major amputation. Thus, it is suggested that among individuals with limb loss due to vascular disease, our sample would be among those more likely to be healthy. That said, this may also be a sub-population which has the most opportunity for intervention to prevent further amputation.
Study strengths include the focus on a novel and important underexamined health indicator, BMI, among an at-risk population, people with dysvascular lower limb amputation. There is increasing interest in promoting physical activity and weight loss among people with lower limb loss, but more research is needed. The study also benefitted from the use of prospectively collected data allowing us to see patterns over one year. More research is needed to build on the preliminary results of this study. Prospective studies using larger samples can help replicate and extend the current findings. Future studies examining obesity among persons with amputation should measure lifestyle habits such as physical activity, sedentary behaviors, and dietary variables in order to accurately examine risk factors for poor functioning and further weight gain. Regardless of the nature of relationships, it is well documented that obesity is related to many health conditions to which this sample is susceptible, including additional amputations, heart disease, and stroke. Thus, using the time of amputation to promote improved lifestyle habits and weight loss could result in a range of physical, mental, and social benefits. Our preliminary findings suggest that weight gain continues post amputation but future studies will need to verify our results using objective measures of BMI, larger samples, and longer follow-up periods. Future studies will be needed to determine whether promoting weight loss following amputation is feasible and can result in health benefits.
Conclusions
Rates of overweight and obesity were high in this sample of adults undergoing lower-extremity amputation. In addition, the majority of amputees gained weight in the year following amputation. The time of amputation may be a prime opportunity for health promotion interventions that target weight loss via improved physical activity and nutrition behaviors.
Implications for Rehabilitation.
People undergoing lower-extremity amputation have high rates of overweight and obesity and continue to gain weight in the year following amputation.
Objective assessment of body mass index (both with and without a prosthetic device) and waist circumference would help future research efforts.
Targeting weight loss post-amputation could improve the health of people with lower-extremity amputations.
Acknowledgments
Declaration of Interest: This material is based upon work supported by the US Department of Veterans Affairs, Office of Research and Development, Rehabilitation Research and Development Merit Review A41241 (Joseph Czerniecki, PI), CDA B4927W (Aaron Turner, PI) CDA 6982 (Alyson Littman, PI) and NIH Kirschstein NRSA award (2T32HD007424-19) for Dori Rosenberg.
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