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Knee Flexion and Daily Activities in Patients following Total Knee Replacement: A Comparison with ISO Standard 14243

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Walking is only one of many daily activities performed by patients following total knee replacement (TKR). The purpose of this study was to examine the hypotheses (a) that subject activity characteristics are correlated with knee flexion range of motion (ROM) and (b) that there is a significant difference between the subject's flexion/extension excursion throughout the day and the ISO specified input for knee wear testing. In order to characterize activity, the number of walking and stair stepping cycles, the time spent with dynamic and stationary activities, the number of activity sequences, and the knee flexion/extension excursion of 32 TKR subjects were collected during daily activity. Flexion/extension profiles were compared with the ISO 14243 simulator input profile using a level crossing classification algorithm. Subjects took an average of 3102 (range: 343-5857) walking cycles including 65 (range: 0-319) stair stepping cycles. Active and passive ROMs were positively correlated with stair walking time, stair step counts, and stair walking sequences. Simulated knee motion according to ISO showed significantly fewer level crossings at the flexion angles 20-40° and beyond 50° than those measured with the monitor. This suggests that implant wear testing protocols should contain more cycles and a variety of activities requiring higher knee flexion angles with incorporated resting/transition periods to account for the many activity sequences.
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Research Article
Knee Flexion and Daily Activities in Patients following Total
Knee Replacement: A Comparison with ISO Standard 14243
Markus A. Wimmer,1William Nechtow,1Thorsten Schwenke,1and Kirsten C. Moisio2
1Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
2Feinberg School of Medicine, Northwestern University, Chicago, IL 60610, USA
Correspondence should be addressed to Markus A. Wimmer; markus a wimmer@rush.edu
Received  December ; Accepted  July 
Academic Editor: George Babis
Copyright ©  Markus A. Wimmer et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Walking is only one of manydaily activities per formed by patients followingtotal knee replacement (TKR). e purpose of this study
was to examine the hypotheses (a) that subject activity characteristics are correlated with knee exion range of motion (ROM) and
(b) that there is a signicant dierence between the subjects exion/extension excursion throughout the day and the ISO specied
input for knee wear testing. In order to characterize activity, the number of walking and stair stepping cycles, the time spent with
dynamic and stationary activities, the number of activity sequences, and the knee exion/extension excursion of  TKR subjects
were collected during daily activity. Flexion/extension proles were compared with the ISO  simulator input prole using a
level crossing classication algorithm. Subjects took an average of  (range: –) walking cycles including  (range: –
) stair stepping cycles. Active and passive ROMs were positively correlated with stair walking time, stair step counts, and stair
walking sequences. Simulated knee motion according to ISO showed signicantly fewer level crossings at the exion angles –
and beyond than those measured with the monitor. is suggests that implant wear testing protocols should contain more cycles
and a variety of activities requiring higher knee exion angles with incorporated resting/transition periods to account for the many
activity sequences.
1. Introduction
Total knee replacement (TKR) surgery has become the most
common total arthroplastic procedure in the United States
with over , surgeries being performed in  and
rising to an expected . million annual surgeries by 
[]. In addition, TKR surgeries are increasingly performed on
younger and more active patients []. In this patient group,
polyethylene wear can be a limiting factor for longevity []. As
for the hip, wear particles generated during sliding contribute
to osteolysis and subsequent loosening of the prosthesis [].
Since this is one of the most common reasons for revision in
TKR [], preclinical wear testing is an important step before
any new TKR device is brought to market.
State-of-the-art knee wear testing is conducted according
to ISO standards - [] and/or - []. ese stan-
dardized protocols presumably mimic the in vivo kinematic
and kinetic conditions of the knee prosthesis during its
lifetime.einputforTKRweartestingisspeciedasa
sequence of gait cycles that are continuously repeated at 1.0±
0.1Hz until  million cycles are reached. is, as commonly
assumed,representstheprostheticlifespanofaboutveyears
in vivo. Indeed, several activity studies on patients with total
hip and/or knee replacement found that subjects walk on
average between . and . million gait cycles annually [
].
However, walking is only one of many daily activities per-
formed by patients following TKR. Other common activities
include standstill with related starting/stopping maneuvers,
stair ascent/descent, chair sitting and rising, lying down to
rest, and a variety of recreational activities. Hence, including
kinematic and kinetic characteristics of these activities into
wear testing may result in a more realistic simulation of
wear. Indeed, better agreement between wear patterns on
simulator tested prostheses and those observed on retrieved
specimens was achieved aer incorporating stair descent into
Hindawi Publishing Corporation
BioMed Research International
Volume 2015, Article ID 157541, 7 pages
http://dx.doi.org/10.1155/2015/157541
BioMed Research International
testing protocols [,]. However, for TKR subjects, duration
and frequency of these additional activities are unknown.
erefore, the purpose of the study was to describe the
frequency and duration of daily physical activities of TKR
subjects during a -hour day using electrogoniometry. In
addition, we chose to follow the exion/extension excur-
sion of the knee prosthesis throughout the day, because
exion/extension movement is an input variable for the
knee simulator, which directly impacts sliding distance and,
thus, wear. e exion/extension excursion is also interesting
from a clinical viewpoint: Active and passive knee exion
ROM are indicators of a patient’s functional status, and knee
ROMiscommonlyusedtoevaluateTKRsurgery[]and
rehabilitation programs []. Although, TKR ROM has been
found uncorrelated with patient satisfaction and perceived
improvement in quality of life [], it is unknown if TKR
ROM is associated with activity prole. We hypothesized that
(a) subject activity characteristics are correlated with knee
exion range of motion (ROM) and (b) there is a signi-
cant dierence between subject exion/extension excursion
movement and the ISO  simulator input.
2. Subjects and Methodology
2.1. Subject Population. Forty subjects were recruited from a
large orthopedic practice (Midwest Orthopaedics, Chicago,
IL) specialized in joint replacement surgery. e study was
approved by the institutional review board, and all subjects
gave informed consent. Potential subjects were identied
from a database of all patients who had received a TKR at the
Medical Center. All participants met the following inclusion
criteria: having received a primary TKR implant of a single
design (Miller-Galante or MGII, Zimmer Inc., Warsaw, IN,
USA), having knee in excellent condition as determined by
latest follow-up, being able to walk without assistive devices,
and being able to live and function independently in their
home. Exclusion criteria were as follows: past or present
history of a neurologic disorder; other medical conditions
aecting their physical function; previous revision surgery.
Six subjects were excluded from the analysis because of cable
or connector failure of the electronic data recording device
and two subjects were excluded because of other technical
errors which truncated the activity data. Data for the remain-
ing  subjects were included in the analysis (Tab l e  ).
2.2. Activity Monitor. e activity monitor utilized hardware
introduced by Morlock et al. []andaportabledatalogger
collecting data from three sensors at Hz. Two inclination
sensors recorded sagittal plane thigh and shank inclinations.
A goniometer connecting the two device segments measured
thekneeexionangle(Figure ). e device weighed less than
 g and did not inhibit movement. Normal clothing was
worn over the device.
Data was streamed to a memory card embedded in the
datalogger.epostprocessingcodewaswritteninMATLAB
(MathWorks, Inc., Natick, MA, USA). Dynamic activities
were classied into walking, stair stepping (ascending and
descending combined), and unrecognized activities based on
a pattern recognition program previously written []and
T : Mean, one standard deviation, and range of demographic
and functional data for subjects included in the analysis (𝑁=32).
Parameter Mean
( SD) Range
Gender M/F / N/A
Age [years] . (.) .–.
Height [cm] . (.) .–.
Mass [kg] . (.) .–.
BMI [kg/m] . (.) .–.
Aected and tested side
(right/le) / N/A
Time between surgery
and data collection
[years]
. (.) .–.
Active knee ROM
Flexion [](12) –
Extension [](3)–
Passive knee ROM
Flexion [](13) –
Extension [](4)–
ROM: range of motion.
Greater
trochanter
Knee joint line
Lateral malleolus
F : Placement of the activity monitor. e following anatom-
ical landmarks served as orientation: greater trochanter, knee joint
line, and lateral malleolus. e electrogoniometer was placed on the
lateral aspect of the knee joint line. e two monitor segments were
aligned along lines connecting the landmarks.
adapted for TKR by H¨
anni et al. [].Lowerandupperexion
angle boundaries for activity recognition were manually set
for each subject using data captured during a calibration
run (Figure ). Stationary activity, for example, lying down,
sitting, and standing, was identied as a period with the thigh
and shank inclination sensors remaining within a ±range
foratleast.secondsandwasfurtherclassiedbasedon
limb inclination (Tab l e  ).
BioMed Research International
T : Classication of stationary activities into lying down,
sitting, and standing was based on shank and thigh inclination.
Activity
Shank
inclination
[]
igh
inclination
[]
Minimum
duration
[second]
Lying down > > 
Sitting > –
Standing – – 
0
60
80
20
40
100
60
40
20
80 28641012
Time (min)
Knee angle
Shank inclination
igh inclination
Sitting
Walking Stair up
Stair down
Walking
Sitting
Standing
AV
LB
UB
Angle ()
F : igh and shank inclination angles, as well as knee
exion angle for various activities of a representative subject during
the calibration procedure. Zero-degree exion and zero-degree
inclination indicate a straight knee and vertical limbs (e.g., during
standing). LB = lower boundary, UP = upper boundary, and AV =
average.
Output of the analysis soware included the number of
sequences for each activity, the time of each sequence, the
overall time for each activity, and the number of cycles for
level and stair walking. A sequence was dened as a contin-
uous activity within its respective boundary conditions. All
data were normalized to  hours to allow for comparison
between subjects.
2.3. Monitor Validation. Twenty out of  subjects were
lmed for approximately two minutes (2.3 ± 0.8 minutes)
while performing sequences of sitting, standing, lying down,
walking, and ascending and descending stairs (simultaneous
to activity monitor recording). Four subjects were lmed
for – minutes while performing routine daily activities.
Two blinded observers, who did not otherwise participate
in the study, independently watched the videos. e number
of cycles walked or climbed was counted; times spent with
lying down, sitting, standing, walking, and stair stepping were
measured; stationary, dynamic, and total activity times were
calculated. Since the intraclass correlation coecient (ICC)
T : Validation data for activity monitor results and observa-
tions of long videos for four subjects. Time values were rounded
to the nearest minute (except for stair walking time). All intraclass
correlation coecients were statistically signicant (𝑃 < 0.04).
Parameter Activity Monitor results Observer
results ICC
Time
[min]
Lying down ±±.
Sitting  ± ±.
Stair walking . ±. . ±. .
Level walking  ±  ± .
Standing  ±  ± .
Total stationary  ±  ± .
Total dynamic  ±  ± .
Overall total  ±  ± .
Steps Level walking  ±  ± .
Stair walking  ±  ± .
between the two observers ranged from . for lying down
time to values greater than . for stair stepping, for both
theshortandlongvideos,theobservers’measurementswere
subsequently averaged. e observer-averaged data were then
used for comparison with the monitor-derived data.
No systematic oset between video and activity monitor
measurements was detected. For the short videos, ICCs for all
parameters, with the exception of sitting time (ICC = .),
exceeded . (range: . to .). For the long videos, ICCs
exceeded . for all parameters (Tab l e  ). e high ICC for sit-
ting time measured from the longer videos (ICC = .) con-
rmed the monitor’s utility to track this activity in the eld.
2.4. Testing Procedure. During a brief clinical examination of
the subjects (at their home) by a licensed physical therapist,
height and weight, as well as active and passive knee exion
range of motion (ROM), were measured. Double sided Velcro
tape (Velcro Inc., Manchester, NH, USA) and Elastikon
athletic tape (Johnson & Johnson Inc., New Brunswick, NJ,
USA) were used to attach the activity monitor to the skin
of the subjects. An elastic tube stocking was pulled over the
aected leg to prevent chang of the device against cloths
and to protect the cables from entanglement. Prior to data
collection, each subject performed an activity calibration
protocol consisting of sitting, standing, level walking, and
stair walking, during which the subject was lmed and sensor
data were recorded. Subsequently, the activity monitor was
restarted to begin the actual data collection. e calibration
procedure was repeated before the monitor was detached at
the end of data collection to detect potential sensor shiing
or other changes. Subjects were asked to keep a diary of
their activities and to follow their usual activity patterns
throughout the day. Data collection was initialized as early
as  minutes of the subject’s waking time and ended as late
as bed time to capture data for approximately  hours.
2.5. Comparison of TKR Flexion/Extension Excursions with
ISO Simulator Prole. e TKR exion/extension curves
from the subjects were compared to the exion/extension
BioMed Research International
0
10
20
30
40
50
60
70
0 20406080100
Count
1
1
1
1
2
1
0
Cycle (%)
Angle ()
F : Level crossing classication of the exion angle 𝛼during
one typical walking cycle (duration: about  s). e count of each
level is summarized to the right.
curve specied in the standard ISO standard by using
the concept of “level crossings.” Referring to a graph of
knee exion (𝑦-axis) versus percent gait cycle (𝑥-axis), a
level crossing is a point where the exion/extension curve
crosses the horizontal line denoting a specied knee angle
level (Figure ). As the exion/extension curve goes up and
down, there can be zero to multiple such crossings for
eachanglelevel.enumberoflevelcrossingsfortheISO
exion/extension curve and for the exion/extension curve of
eachsubjectwascountedatthe0,10, 20, . . . , 140angle levels.
Only upward crossings were counted (Figure ). Assuming
an identical number of walking cycles per day, the ISO-
simulated knee exion/extension level crossings were now
compared to those of the TKR subjects.
2.6. Statistical Analysis. All statistical tests were performed
in SPSS Version . (SPSS Inc., Chicago, IL, USA). Aer
normalization to  hours, the mean and standard deviations
were computed for the relative amount of time spent with
each activity, the occurring sequences for each activity, and
thenumberofstepsforlevelwalkingandstairwalking.
Linear regression models were used to identify associations
between these monitor-derived values and subject charac-
teristics including time past surgery, BMI, height, mass, age,
and active and passive ROM. One-sample 𝑡-tests were used
to detect signicant dierences in number of level crossings
between the in vivo activitydataandthevaluefortheISO
standard at each angular level. e signicance level for all
statistical tests was set to .. e Bonferroni correction was
applied for tests with multiple comparisons.
3. Results
e average total test duration was 11.3 ± 1.2 hours (range:
.–. hours) out of which 9.3 ± 1.2 hours were identied
as stationary activities and 0.9 ± 0.5 hours consisted of
dynamic activities. e remainder of 1.1 ± 0.4 hours could
not be allocated by the analysis soware and was marked
“unrecognized.” e most frequently performed activity,
according to sequence counts, was standing, followed by
level walking, sitting, stair walking, and lying down (Ta b l e  ).
Subjects performed an average of 3102 ±1553 walking cycles
perhoursofdailyactivity,outofwhich65 ± 70 were
stair cycles (.%) (Tabl e ). e number of walking cycles
T : Mean, one standard deviation, and range of relative time,
step counts, and number of sequences for all tested activities for all
included subjects (𝑁=32). Time and steps were normalized to 
hours.
Parameter Activity Mean
( SD) Range
Time [%]
Lying down . (.) .–.
Sitting . (.) .–.
Standing . (.) .–.
Level walking . (.) .–.
Stair walking . (.) .–.
Unrecognized . (.) .–.
Steps
Stair walking  () –
Level walking  () –
Total  () –
Pedometer  () –
Sequence
Lying down (3)
Sitting  (31)
Standing  () –
Level walking  () –
Stair walking (14)
Unrecognized  () –
correlated with the number of walking sequences (𝑟 = 0.743;
𝑃 < 0.001). On average, subjects took 8.3± 3.0 walking cycles
per walking sequence. Subjects spent signicantly more time
sitting than performing any other activity (Tab l e  ;𝑃<
0.001). Subjects spent signicantly less time walking than
standing (Table ;𝑃 < 0.001).
Active knee exion ROM (as measured during the clinical
exam) correlated with stair walking time (𝑟 = 0.532,𝑃=
0.002), stair walking counts (𝑟 = 0.551,𝑃 = 0.001), and
stair walking sequences (𝑟 = 0.556,𝑃 = 0.001). Similarly,
passive knee exion ROM correlated with stair walking time
(𝑟 = 0.534,𝑃 = 0.002), stair walking counts (𝑟 = 0.535,
𝑃 = 0.002), and stair walking sequences (𝑟 = 0.538,𝑃=
0.001). Time between surgery and activity analysis did not
correlatewithanyofthefunctionalvariables.Nostatistically
signicant dierence between female and male subjects for
any of the variables was found, except for height (𝑃 < 0.001).
e level crossing classication indicated that the popula-
tion as a whole crossed exion levels ranging from to 
approximating a log-normal distribution (Figure ). e 
exion level was crossed most frequently with an average of
6789 ± 4376 crossings. e level was crossed the least,
averaging only 2±11level crossings in the day. However, not
all TKR subjects crossed all levels during daily activity. e
levelwascrossedbysubjects(althoughonlybysixata
relevant number of >), and the level was crossed by
only three subjects. All TKR subjects crossed levels between
and . ere was a signicant correlation between the
subjects’ maximum level crossed and the measured active or
passive ROM (𝑃 < 0.001).
e range of crossed levels for ISO was much smaller (
to ) following a nonnormal distribution. Comparing it to
BioMed Research International
Average subject prole
ISO prole
0
2
4
6
8
10
12
0 140130
12011010090
8070605040302010
Counts [103]
Flexion angle ()
F : Mean ( SD) number of level crossings for exion angle
levelsfromto
. Standard deviations are indicated for the
average counts of the activity analysis patient population. e ISO
prole count was extrapolated from the exion-extension curve as
provided by ISO  (ISO--, ) and the average number
of walking steps taken by the subjects during a -hour period.
subject data, the level crossing pattern was shied to the le
(i.e., towards lower exion angles; see Figure ). Most level
crossings were found for the angle (instead of the 
angle). Beyond the angle there were no crossings at all.
e average number of crossings was higher for the subject
population at all exion angles beyond . is nding was
signicant (𝑃 < 0.01), except for the angle (Figure ).
4. Discussion
is study provides information on the frequency and dura-
tion of daily physical activities performed by TKR patients
during a -hour day. Subjects spent most of the time sitting,
followed by standing and walking. e large number of
activity sequences (mean total number of sequences: )
indicates that common daily activities such as standing
are interspersed with frequent transitions between activities
resulting in ever changing in vivo loading proles for the
implant. More distinct sequences of standing were recorded
than of any other activity. e results suggest that standing is
a common resting state between various dynamic activities.
In simulation experiments of total hip joints, resting periods
increased the starting friction, indicating lubricant starva-
tion, potentially leading to increased wear []. e results of
this study suggest that one resting period should theoretically
be included on the simulator every . cycles to properly
reect the dynamic activity prole of walking. Subjects who
took more walking cycles did so during a greater number of
sequences, and the number of walking cycles per sequence
showed a relatively small variability. ese results suggest that
wear proles of more active patients could be simulated by
longer testing times.
e total numbers of walking cycles taken per day in
this subject population are within the ranges reported in the
literature. A recent meta-analysis by Naal and Impellizzeri
[], which included  patients with total joint replace-
ment (summarizing data from  pedometer/accelerometer
studies), found a weighted mean of  (% CI: –
) walking cycles per day. is compares well with our
own average of  walking cycles per day, particularly if
the somewhat older age of our subject population is taken
into account. e number also agrees with another meta-
analysis of healthy individuals: Bohannon []found
walking cycles in individuals over  years old. Since our
TKR patients are expected to take 1.13± 0.56 million walking
cycles per year, including about , stair stepping cycles,
they are however more active than what is normally assumed
in wear simulations. In general, a large variability in activity
and step patterns was observed between subjects. e most
active patient is estimated to take . million walking cycles
per year, including , stair stepping cycles. Similar
results have been reported for patients following total hip
arthroplasty [].elargevariabilityinthenumberofwaking
cycles per day suggests that results from wear tests are only
representative for some subjects and that larger total numbers
ofcyclesperweartestareneededtosimulatewearpatternsfor
more active patients.
Flexion ROM is an important outcome variable in TKR
since many daily activities depend on it. As has been recently
summarized by Fu et al. [], a higher ROM than walking is
necessary for stair or chair maneuvers (–), kneeling
or squatting (–), bathtub use (), and gardening
(>). Not surprisingly, in this study, there was a high
correlation between the maximum exion angle measured
during daily activity and the ROM measured during clinical
examination. Interestingly, subjects with greater active and
passive knee exion ROM also spent more time level walking
and stair stepping. However, it is unclear if more active
patients had greater knee exion ROM because they were
more active or if greater knee exion ROM facilitated a
greater activity level. Nevertheless, the association between
knee exion ROM and activity level should be taken into
consideration during rehabilitation programs following TKR
surgery. e ndings are also interesting in the context of
the ongoing debate about the usefulness of high-exion knee
implants [,]. Based on this data, active patients might
very well benet from it. Future studies comparing high-
exion and standard TKR should therefore stratify for activity
level to break the stalemate.
elevelcrossinganalysisforactivitiesduringa-
hour period revealed a large range of knee exion during
daily activities. e most frequently crossed angle was 
of knee exion in our subject population and some subjects
exed their prosthetic knee up to .Incontrast,themost
frequently crossed angle as specied in the ISO  standard
was of knee exion with a maximum knee exion angle
crossing at . While it is well known that the ISO standard is
representative for walking activities, the results of this study
clearly show that the ranges of knee exion experienced in
vivo arenotfullyrepresentedbytheISOprole.Hence,the
ASTM F committee has become active in the development
of a standard guide, which will include loading proles other
than walking (personal communication). Since the medial
and lateral femoral radii of the TKR typically decrease with
BioMed Research International
higher exion angle, stresses at the polyethylene plateau may
increase leading to more surface damage. ese dierences
may explain the discrepancies between wear patterns on
retrieval prostheses and those on simulator wear tested
prostheses [,]. Hence, a modied simulator input prole
entailing the exion prole of activities other than walking is
necessary to simulate in vivo loading and wear of the implant.
Recently, detailed in vivo loading data for daily activities
in patients following TKR has become available [].
While these studies specied the in vivo load magnitude and
knee exion angles for dierent activities of daily living, the
data in these studies were captured from a relatively small
patientpoolwithinstrumentedkneeimplantsandusually
collected in a laboratory environment, except for D’Lima et
al. [] who conducted some eld measurements for specic
activities. However, combining the contact force information
reportedintheliteraturewiththeactivityprolesobtained
in this study greatly improves the understanding of in vivo
loading proles during daily activities in patients following
TKR. Based on the results of the present study, a ratio of  : 
of number of walking cycles to the number of stair stepping
cycles would be appropriate to represent loading patterns
during locomotion of daily living.
e study has several limitations. All subjects in this study
had received a Miller-Galante or MGII implant. It is possible
that activity proles dier between implant type and model,
that they change over time, and that these changes may aect
implantwearpatterns.Also,theadvancedageofthesubject
population (mean: . years) may have aected the activity
pattern; however, as discussed above, the observed number
of walking cycles was well within the range reported in the
literature. us, we believe this should be similarly true for
other outcome variables of this study.
e amount of unrecognized activity (.% of the total
measurement time) was unexpectedly high. A detailed anal-
ysis of the recorded waveforms of several subjects revealed
that this unrecognized data set consisted mostly of transitions
from one activity to another. Explicit denitions for transi-
tions between activities would improve the proper allocation
oftime.Further,somepatientswalkedwithtwodistinguish-
able types of step patterns: normal walking steps with a high
exionangleandso-called“nesteps”characterizedbylower
knee exion angle. Fine steps with a peak exion angle below
the lower boundary of level walking were not recognized
and classied as “unrecognized.” ese ne steps were oen
taken in conned spaces such as the kitchen as indicated by
the patients’ diaries. Future renements of the recognition
algorithm should incorporate these additional distinctions
of dynamic activities. Finally, activity and exion/extension
monitoring of the knee occurred without simultaneous
recording of knee contact force, which comprises another
important input variable for knee wear testing. Future studies
are necessary to determine the specic loading prole occur-
ring at exion angles >.
5. Conclusion
In conclusion, walking and stair stepping accounted for about
% of the monitoring time, with a ratio of  : . Subjects
with a higher knee ROM climbed more stairs. While level
walking is the dynamic activity that the articial implant will
have to endure the most, transition periods between activities
are quite common. Walking sequences oen include periods
of standing. e knee exion excursion during  hours of
daily activity in patients following TKR includes knee exion
angles ranging from to ,whichisnotrepresentedby
the current ISO standards. Taken together, simulated implant
wear testing should contain resting or transition periods
between activities and a larger range of activities such as
stair walking and chair maneuvers and include more loading
cycles than specied in the current standard.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
Acknowledgments
e authors would like to thank Professor Michael Morlock
for helpful discussions about the technical aspects of the
activity monitor, Robert Trombley and Anand Joshi for
performingvideotapeanalyses,andDrs.KharmaFoucher
and Annegret M ¨
undermann for their assistance in data
interpretation and paper editing. is study was funded in
part by NIH (R AR and R AR).
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... Due to this, overweight or obesity is strongly associated with disease progression of KOA [2,3], obesity has an increased risk of TKR [4,5], and the prevalence of obesity in patients undergoing TKR surgery is expected to increase [6]. In addition, obesity is a predominant risk factor for post-surgery complications and may affect short-term or long-term outcomes after TKR [7,8], among which the recovery in knee flexion range of motion (ROM) is of particular interest [9][10][11][12][13]. However, the effects of obesity on surgery outcomes, including ROM, regain after TKR remains conflict [1]. ...
... The total sum scores for the MNA-SF range from 0 to 14 points, with 14 points indicating the best nutritional state possible [52]. An MNA-SF score of ≤7, [8][9][10][11], and ≥12 points indicates malnutrition, possible malnutrition, and well-nourished, respectively. In the present study, the patients who obtained a total score of MNA-SF < 12 points were identified as having potential risk of undernutrition [53]. ...
... Poor surgical outcome was identified based on knee flexion ROM, which is an indicator for clinicians to determine the success of TKR surgery [9][10][11][12][13]. In the present study, a cut-off value of 125 degrees of knee flexion was used for poor outcome following TKR based on the following reasons: (1) a knee flexion of 125 degrees permits older individuals to perform the majority of activities in daily life [65][66][67]; (2) a knee flexion ROM < 125 degrees leads to function limitations after TKR [21]; and (3) those who have a knee flexion ROM < 125 degrees tend to experience lower walking capability after TKR [68]. ...
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1) Background: Knee osteoarthritis (KOA) and aging are associated with high sarcopenia risk; sarcopenia may further affect outcomes after total knee replacement (TKR). Elastic resistance exercise training (RET) limits muscle attenuation in older adults. We aimed to identify the effects of post-TKR elastic RET on lean mass (LM) and functional outcomes in overweight and obese older women with KOA by using the brief International Classification of Functioning, Disability and Health Core Set for osteoarthritis (Brief-ICF-OA). (2) Methods: Eligible women aged ≥60 years who had received unilateral primary TKR were randomly divided into an experimental group (EG), which received postoperative RET twice weekly for 12 weeks, and a control group (CG), which received standard care. The primary and secondary outcome measures were LM and physical capacity, respectively, and were linked to the Brief-ICF-OA. The assessment time points were 2 weeks prior to surgery (T 0) and postoperative at 1 month (T 1 ; before RET) and 4 months (T 2 ; upon completion of RET) of follow-up. An independent t test with an intention-to-treat analysis was conducted to determine the between-group differences in changes of outcome measures at T 1 and T 2 from T 0. (3) Results: Forty patients (age: 70.9 ± 7.3 years) were randomly assigned to the EG (n = 20) or CG (n = 20). At T 2 , the EG exhibited significantly greater improvements in leg LM (mean difference (MD) = 0.86 kg, p = 0.004) and gait speed (MD = 0.26 m/s, p = 0.005) compared with the CG. Furthermore, the EG generally obtained significantly higher odds ratios than the CG for treatment success for most Brief-ICF-OA categories (all p < 0.001). Conclusions: Early intervention of elastic RET after TKR yielded positive postoperative outcomes based on the Brief-ICF-OA. The findings of this study may facilitate clinical decision-making regarding the optimal post-TKR rehabilitation strategy for older women with KOA.
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Prior studies have reported a negative effect on both clinical outcomes and patient-reported outcome measures (PROMS) following joint line elevation (JLE) in cruciate-retaining (CR) total knee arthroplasty (TKA) and posterior stabilized (PS) TKA designs. This experimental study was aimed to quantify the effect of JLE on in vivo knee kinematics in patients with bicruciate retaining (BCR) TKA during strenuous activities. Thirty unilateral BCR TKA patients were evaluated during single-leg deep lunge and sit-to-stand using a validated combined computer tomography and dual fluoroscopic imaging system. Correlation analysis was performed to quantify any correlations between JLE and in vivo kinematics, as well as PROMS. There was a significant negative correlation between JLE and maximum flexion angle during single-leg deep lunge (ρ = −0.34, p = 0.02), maximum varus joint angles during single-leg deep lunge (ρ = −0.37, p = 0.04), and sit-to-stand (ρ = −0.29, p = 0.05). There was a significant negative correlation between JLE and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (ρ = −0.39, p = 0.01) and knee disability and osteoarthritis outcome score physical function (KOOS-PS; ρ = −0.33, p = 0.03). The JLE that yields a significant loss in PROMS and maximum flexion angles were 2.6 and 2.3 mm, respectively. There was a linear negative correlation of JLE with both in vivo knee kinematics and PROMS, with changes in JLE of greater than 2.6 and 2.3 mm, leading to a clinically significant loss in PROMS and maximum flexion angles, respectively, suggesting an increased need to improve surgical precision to optimize patient outcomes following BCR TKA.
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Objective Total knee arthroplasty (TKA) is the gold-standard treatment for end-stage knee osteoarthritis, and the primary expectations are reduced pain and improved function. However, there is conflicting evidence regarding functional changes post-TKA. Commonly, functional changes are measured using Oxford Knee Score (OKS). No previous study has investigated physical behaviour (PB) changes in terms of volume and patterns post-TKA. The aims of this study were to explore volume and pattern changes in PB following TKA using an objective tool and to assess the correlation between this and OKS. Design An activPAL measured the PB of individuals on a waiting list for TKA for a period of 7–8 days pre-TKA, and for the same length of time at 12 months post-TKA. OKS was completed at similar follow-up time points. Results Thirty-three individuals completed the study, where stepping time, the number of steps and the time spent on moderate to vigorous physical activity (MVPA) (>100 steps/minute) improved significantly post-TKA p = 0.0001. Steps at 12 months post-TKA improved by 45.6% (from 4,240 to 6,174) and stepping time increased by 38.8% (from 0.98 to 1.36 hours). MVPA improved by 35 minutes at 12 months (from 6.6 to 41.7 minutes). There were no significant correlations between PB and OKS. Conclusion This is the first study to explore PB volumes and event-based patterns post-TKA. Activity improved in terms of volume and patterns. No correlation was found between OKS and ActivPAL, which emphasises the need to use objective methods in addition to patient-reported outcome measures.
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Purpose Higher revision rates were shown in varus- or valgus-positioned tibias in unicompartmental knee arthroplasty (UKA), but more than 15% of UKA prostheses are implanted with more than 5° of varus or valgus. This study aimed to analyze the wear rate in UKA when implanting the tibial component in either varus or valgus position versus a neutral placement at 90° to the tibial anatomical axis. The study hypothesized that a 5° varus or valgus positioning of the tibial plateau will generate less wear compared to a neutral alignment. Methods Wear was experimentally analyzed on a medial anatomical fixed-bearing unicompartmental knee prosthesis (Univation, Aesculap, Germany) in vitro with a customized, four-station, servohydraulic knee wear simulator, reproducing the walking cycle. The forces, loading and range of motion were applied as specified in the ISO 14243–1:2002, 5 million cycles were analyzed. The tibial components of the medial prostheses were inserted in a neutral position, with 5° varus, and 5° valgus (n = 3, each group). Results The wear rate decreased significantly with a 5° varus positioning (6.30 ± 1.38 mg/million cycles) and a 5° valgus positioning (4.96 ± 2.47 mg/million cycles) compared to the neutral position (12.16 ± 1.26 mg/million cycles) (p < 0.01 for the varus and the valgus position). The wear area on the inlay was slightly reduced in the varus and valgus group. Conclusion A varus or valgus “malpositioning” up to 5° will not lead to an increased wear. Wear was even less because of the reduced articulating contact area between the inlay and the femur. A slight varus positioning of the tibial component (parallel to the anatomical joint line) positioning can be advocated from a point of wear. Level of evidence Experimental study.
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The aim of this study was to determine how representative wear scars of simulator-tested polyethylene (PE) inserts compare with retrieved PE inserts from total knee replacement (TKR). By means of a non-parametric Self-Organizing Feature Map (SOFM), wear scar images of 21 postmortem and 54 revision-retrieved components were compared with six simulator-tested components that were tested either in displacement or load control according to ISO protocols. The SOFM network was then trained with the wear scar images of postmortem-retrieved components, since those are considered well-functioning at the time of retrieval. Based on this training process, eleven clusters were established, suggesting considerable variability among wear scars despite an uncomplicated loading history inside their hosts. The remaining components (revision-retrieved and simulator-tested) were then assigned to these established clusters. Six out of five simulator components were clustered together, suggesting that the network was able to identify similarities in loading history. However, the simulator-tested components ended up in a cluster at the fringe of the map containing only 10.8% of retrieved components. This may suggest that current ISO testing protocols were not fully representative of this TKR population, and protocols that better resemble patients’ gait after TKR containing activities other than walking may be warranted. Keywords: retrieval analysis, knee-wear-simulation, self-organizing-feature maps, clustering, data-mining
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Concern exists regarding the long-term durability and effectiveness of total knee arthroplasty in young patients. We reviewed our experience with total knee arthroplasty in patients fifty-five years old and younger with severe osteoarthritis to determine the long-term outcomes. One hundred and fourteen total knee arthroplasties were performed in eighty-eight patients at an average patient age of fifty-one years. Clinical outcomes, survival analysis, and radiographs were all reviewed at the most recent follow-up. One hundred and eight knees (eighty-four patients) were followed up from May 2011 to 2012. At thirty years, survivorship without revision for any cause was 70.1% (twenty-five revisions) and survivorship with failure defined as aseptic revision of the tibial or femoral components was 82.5%. At thirty years, a significant difference existed in the survivorship free from tibial or femoral aseptic revision (p = 0.003) between the non-modular Insall-Burstein I component (92.3%) and the modular Insall-Burstein II component (68.3%). All patients were evaluated at an average time from the index total knee arthroplasty to the latest follow-up of 25.1 years (range, twenty to thirty-five years). Clinical evaluation was obtained in thirty-six patients with forty-five total knee arthroplasties. The average Hospital for Special Surgery score had improved from 57.9 points preoperatively to 85.3 points. The average Knee Society score was 87.4 points and the average Knee Society functional score was 62.1 points; the average knee motion was 110°. The mean Tegner and Lysholm activity score improved from 1.5 points preoperatively to 3.0 points. Radiographic review of forty-two knees that had undergone total knee arthroplasty demonstrated a mean 3.2° of valgus, with no cases of radiographically loose components. Total knee arthroplasty with use of a cemented posterior stabilized system, particularly a non-modular Insall-Burstein I design, was an effective treatment option with durable results for end-stage symptomatic osteoarthritis in this young cohort. These data should provide comparison for modern total knee arthroplasties and alternative procedures in young patients.
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The loads acting in knee joints must be known for improving joint replacement, surgical procedures, physiotherapy, biomechanical computer simulations, and to advise patients with osteoarthritis or fractures about what activities to avoid. Such data would also allow verification of test standards for knee implants. This work analyzes data from 8 subjects with instrumented knee implants, which allowed measuring the contact forces and moments acting in the joint. The implants were powered inductively and the loads transmitted at radio frequency. The time courses of forces and moments during walking, stair climbing, and 6 more activities were averaged for subjects with I) average body weight and average load levels and II) high body weight and high load levels. During all investigated activities except jogging, the high force levels reached 3,372-4,218N. During slow jogging, they were up to 5,165N. The peak torque around the implant stem during walking was 10.5 Nm, which was higher than during all other activities including jogging. The transverse forces and the moments varied greatly between the subjects, especially during non-cyclic activities. The high load levels measured were mostly above those defined in the wear test ISO 14243. The loads defined in the ISO test standard should be adapted to the levels reported here. The new data will allow realistic investigations and improvements of joint replacement, surgical procedures for tendon repair, treatment of fractures, and others. Computer models of the load conditions in the lower extremities will become more realistic if the new data is used as a gold standard. However, due to the extreme individual variations of some load components, even the reported average load profiles can most likely not explain every failure of an implant or a surgical procedure.
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Tibiofemoral forces are important in the design and clinical outcomes of TKA. We developed a tibial tray with force transducers and a telemetry system to directly measure tibiofemoral compressive forces in vivo. Knee forces and kinematics traditionally have been measured under laboratory conditions. Although this approach is useful for quantitative measurements and experimental studies, the extrapolation of results to clinical conditions may not always be valid. We therefore developed wearable monitoring equipment and computer algorithms for classifying and identifying unsupervised activities outside the laboratory. Tibial forces were measured for activities of daily living, athletic and recreational activities, and with orthotics and braces, during 4 years postoperatively. Additional measurements included video motion analysis, EMG, fluoroscopic kinematic analysis, and ground reaction force measurement. In vivo measurements were used to evaluate computer models of the knee. Finite element models were used for contact analysis and for computing knee kinematics from measured knee forces. A third-generation system was developed for continuous monitoring of knee forces and kinematics outside the laboratory using a wearable data acquisition hardware. By using measured knee forces and knee flexion angle, we were able to compute femorotibial AP translation (-12 to +4 mm), mediolateral translation (-1 to 1.5 mm), axial rotation (-3° to 12°), and adduction-abduction (-1° to +1°). The neural-network-based classification system was able to identify walking, stair-climbing, sit-to-stand, and stand-to-sit activities with 100% accuracy. Our data may be used to improve existing in vitro models and wear simulators, and enhance prosthetic designs and biomaterials.
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Qualitative research studies regarding physical activity in patients undergoing total joint arthroplasty (TJA) unfortunately are sparse in the current literature. To provide a foundation for future investigations, we performed a systematic review to identify the different instruments used to quantify physical activity in patients undergoing TJA and to determine how active these patients really are. We systematically reviewed the literature on the bibliographic databases Medline, Cochrane Library, and EMBASE published until September 2008, focusing on studies assessing total physical activity in patients after or undergoing TJA. Results of those studies quantifying physical activity using accelerometers and pedometers were combined using meta-analytic methods. In the 26 studies included (n = 2460 patients), motion sensors and recall questionnaires were most commonly used. The research aims and goals varied widely among the studies and the results mainly were descriptive. Studies quantifying physical activity using pedometers and accelerometers suggested a weighted mean of 6721 steps/day (95% confidence interval [CI], 5744-7698). Steps per day determined by accelerometers were 2.2 times the steps measured by pedometers. Metaregression showed that walking activity decreased by 90 steps/day (95% CI, -156 to -23) every year of patient age. These results suggest patients undergoing TJA are less active than recommended to achieve health-enhancing activity levels (greater than 10,000 steps/day), but they appear more active than normally assumed in typical wear simulations. Future investigations have to evolve more standardization in the assessment and reporting of physical activity in TJA patients.
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The application of high-flex prosthesis in total knee arthroplasty (TKA) is an area of continuing debate. Thus, we conducted a meta-analysis of randomized controlled trials (RCTs). A literature search was performed in PubMed, EMBASE and the Cochrane database. 10 trials involving 1230 knee joints were eligible for our meta-analysis. No significant difference was observed between the two designs regarding postoperative range of flexion, clinical scores, quality of life outcomes, or complication rate. Moreover, the advantage of high-flex implants for patients with high preoperative range remained not statistically significant and high-flex design in NexGen system showed a marginal improvement in the postoperative range of flexion. Based on current findings, high-flex prosthesis did not appear to confer any benefit as compared to standard prosthesis. Copyright © 2014 Elsevier Inc. All rights reserved.
Article
Purpose NexGen Legacy Posterior Stabilized high-flexion prostheses (LPS-Flex) have been popularized as an alternative to NexGen standard prostheses (LPS) in total knee arthroplasty (TKA). Advocates of this new generation prosthesis suggest improved postoperative knee flexion. The purpose of this study was to summarize the best evidence for comparing the range of motion (ROM) and functional outcomes of LPS-Flex prostheses and LPS in TKA. Methods Electronic databases were systematically searched to identify relevant randomized controlled trials (RCTs). The last date for our research was July 2014. Our search strategy was followed the requirements of the Cochrane Library Handbook. The methodological quality was assessed, and the data were extracted independently by two authors. Results Nine studies that included 978 knees met our inclusion criteria for review. The results showed that there was larger postoperative ROM (1.62, 95 % CI 0.52–2.72) in the LPS-Flex group than in the LPS group. There was not a statistically significant difference in the clinical functional scores and complications between the LPS-Flex group and the LPS group in TKA. The pooled mean differences were as follows: total KSS, −0.64 (95 % CI −1.41 to 0.13); functional KSS, −0.53 (95 % CI −1.51 to 0.45); HSS, 0.23 (95 % CI −0.87 to 1.33); complications, 0.49 (95 % CI 0.20–1.24); and radiolucent lines, 1.56 (95 % CI 0.68–3.55). Conclusions The preliminary results indicate that the NexGen LPS-Flex prosthesis provides an alternative to the NexGen LPS prosthesis, with greater range of motion and without severe complications or radiographic outcomes. The clinical advantages were not shown in the KSS or the HSS. Thus, the selection of a high-flexion prosthesis should depend on the characteristics of the patient, particularly high motivation and poor preoperative ROM. The potential benefits in the medium- and long-term outcomes require confirmation by larger, multicenter and well-conducted RCTs. Level of evidence Therapeutic study, Level I.
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Few studies have explored the role of the National Health Expenditure and macroeconomics on the utilization of total joint replacement. The economic downturn has raised questions about the sustainability of growth for total joint replacement in the future. Previous projections of total joint replacement demand in the United States were based on data up to 2003 using a statistical methodology that neglected macroeconomic factors, such as the National Health Expenditure. Data from the Nationwide Inpatient Sample (1993 to 2010) were used with United States Census and National Health Expenditure data to quantify historical trends in total joint replacement rates, including the two economic downturns in the 2000s. Primary and revision hip and knee arthroplasty were identified using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification. Projections in total joint replacement were estimated using a regression model incorporating the growth in population and rate of arthroplasties from 1993 to 2010 as a function of age, sex, race, and census region using the National Health Expenditure as the independent variable. The regression model was used in conjunction with government projections of National Health Expenditure from 2011 to 2021 to estimate future arthroplasty rates in subpopulations of the United States and to derive national estimates. The growth trend for the incidence of joint arthroplasty, for the overall United States population as well as for the United States workforce, was insensitive to economic downturns. From 2009 to 2010, the total number of procedures increased by 6.0% for primary total hip arthroplasty, 6.1% for primary total knee arthroplasty, 10.8% for revision total hip arthroplasty, and 13.5% for revision total knee arthroplasty. The National Health Expenditure model projections for primary hip replacement in 2020 were higher than a previously projected model, whereas the current model estimates for total knee arthroplasty were lower. Economic downturns in the 2000s did not substantially influence the national growth trends for hip and knee arthroplasty in the United States. These latest updated projections provide a basis for surgeons, hospitals, payers, and policy makers to plan for the future demand for total joint replacement surgery.
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Aseptic loosening and other wear-related complications are one of the most frequent late reasons for revision of total knee arthroplasty (TKA). Periprosthetic osteolysis (PPOL) predates aseptic loosening in many cases indicating the clinical significance of this pathogenic mechanism. A variety of implant-, surgery-, and host-related factors have been delineated to explain the development of PPOL. These factors influence the development of PPOL due to changes in mechanical stresses within the vicinity of the prosthetic device, excessive wear of the polyethylene liner, and joint fluid pressure and flow acting on the peri-implant bone. The process of aseptic loosening is initially governed by factors such as implant/ limb alignment, device fixation quality, and muscle coordination/ strength. Later large numbers of wear particles detached from TKAs trigger and perpetuate particle disease, as highlighted by progressive growth of inflammatory/ granulomatous tissue around the joint cavity. An increased accumulation of osteoclasts at the bone-implant interface, an impairment of osteoblast function, mechanical stresses, and an increased production of joint fluid contribute to bone resorption and subsequent loosening of the implant. In addition, hypersensitivity and adverse reactions to metal debris may contribute to aseptic TKA failure but should be determined more precisely. Patient activity level appears to be the most important factor when the long-term development of PPOL is considered. Surgical technique, implant design, and material factors are the most important preventative factors because they influence both the generation of wear debris and excessive mechanical stresses. New generations of bearing surfaces and designs for TKA should carefully address these important issues in extensive preclinical studies. Currently, there is little evidence that PPOL can be prevented with pharmacological interventions.