Views of physiatrists and physical therapists on the use of gait-training robots for stroke patients
Identifieur interne : 000006 ( Pmc/Checkpoint ); précédent : 000005; suivant : 000007Views of physiatrists and physical therapists on the use of gait-training robots for stroke patients
Auteurs : Chang Gu Kang ; Min Ho Chun ; Min Cheol Chang ; Won Kim ; Kyung Hee DoSource :
- Journal of Physical Therapy Science [ 0915-5287 ] ; 2016.
Abstract
[Purpose] Gait-training robots have been developed for stroke patients with gait disturbance. It is important to survey the views of physiatrists and physical therapists on the characteristics of these devices during their development. [Subjects and Methods] A total of 100 physiatrists and 100 physical therapists from 38 hospitals participated in our questionnaire survey. [Results] The most common answers about the merits of gait-training robots concern improving the treatment effects (28.5%), followed by standardizing treatment (19%), motivating patients about treatment (17%), and improving patients’ self-esteem (14%). The subacute period (1–3 months post-stroke onset) was most often chosen as the ideal period (47.3%) for the use of these devices, and a functional ambulation classification of 0–2 was the most selected response for the optimal patient status (27%). The preferred model was the treadmill type (47.5%) over the overground walking type (40%). The most favored commercial price was $50,000–$100,000 (38.3%). The most selected optimal duration for robot-assisted gait therapy was 30–45 min (47%), followed by 15–30 min (29%), 45–60 min (18%), ≥ 60 min (5%), and < 15 min (1%). [Conclusion] Our study findings could guide the future designs of more effective gait-training robots for stroke patients.
Url:
DOI: 10.1589/jpts.28.202
PubMed: 26957758
PubMed Central: 4756004
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Views of physiatrists and physical therapists on the use of gait-training
robots for stroke patients</title><author><name sortKey="Kang, Chang Gu" sort="Kang, Chang Gu" uniqKey="Kang C" first="Chang Gu" last="Kang">Chang Gu Kang</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Chun, Min Ho" sort="Chun, Min Ho" uniqKey="Chun M" first="Min Ho" last="Chun">Min Ho Chun</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Chang, Min Cheol" sort="Chang, Min Cheol" uniqKey="Chang M" first="Min Cheol" last="Chang">Min Cheol Chang</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Kim, Won" sort="Kim, Won" uniqKey="Kim W" first="Won" last="Kim">Won Kim</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Hee Do, Kyung" sort="Hee Do, Kyung" uniqKey="Hee Do K" first="Kyung" last="Hee Do">Kyung Hee Do</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26957758</idno><idno type="pmc">4756004</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756004</idno><idno type="RBID">PMC:4756004</idno><idno type="doi">10.1589/jpts.28.202</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000516</idno><idno type="wicri:Area/Pmc/Curation">000516</idno><idno type="wicri:Area/Pmc/Checkpoint">000006</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Views of physiatrists and physical therapists on the use of gait-training
robots for stroke patients</title><author><name sortKey="Kang, Chang Gu" sort="Kang, Chang Gu" uniqKey="Kang C" first="Chang Gu" last="Kang">Chang Gu Kang</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Chun, Min Ho" sort="Chun, Min Ho" uniqKey="Chun M" first="Min Ho" last="Chun">Min Ho Chun</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Chang, Min Cheol" sort="Chang, Min Cheol" uniqKey="Chang M" first="Min Cheol" last="Chang">Min Cheol Chang</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Kim, Won" sort="Kim, Won" uniqKey="Kim W" first="Won" last="Kim">Won Kim</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Hee Do, Kyung" sort="Hee Do, Kyung" uniqKey="Hee Do K" first="Kyung" last="Hee Do">Kyung Hee Do</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Physical Therapy Science</title><idno type="ISSN">0915-5287</idno><idno type="eISSN">2187-5626</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>[Purpose] Gait-training robots have been developed for stroke patients with gait
disturbance. It is important to survey the views of physiatrists and physical therapists
on the characteristics of these devices during their development. [Subjects and Methods] A
total of 100 physiatrists and 100 physical therapists from 38 hospitals participated in
our questionnaire survey. [Results] The most common answers about the merits of
gait-training robots concern improving the treatment effects (28.5%), followed by
standardizing treatment (19%), motivating patients about treatment (17%), and improving
patients’ self-esteem (14%). The subacute period (1–3 months post-stroke onset) was most
often chosen as the ideal period (47.3%) for the use of these devices, and a functional
ambulation classification of 0–2 was the most selected response for the optimal patient
status (27%). The preferred model was the treadmill type (47.5%) over the overground
walking type (40%). The most favored commercial price was $50,000–$100,000 (38.3%). The
most selected optimal duration for robot-assisted gait therapy was 30–45 min (47%),
followed by 15–30 min (29%), 45–60 min (18%), ≥ 60 min (5%), and < 15 min (1%).
[Conclusion] Our study findings could guide the future designs of more effective
gait-training robots for stroke patients.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Mauritz, Kh" uniqKey="Mauritz K">KH Mauritz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="You, Yy" uniqKey="You Y">YY You</name></author><author><name sortKey="Chung, Sh" uniqKey="Chung S">SH Chung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="J Rgensen, Hs" uniqKey="J Rgensen H">HS Jørgensen</name></author><author><name sortKey="Nakayama, H" uniqKey="Nakayama H">H Nakayama</name></author><author><name sortKey="Raaschou, Ho" uniqKey="Raaschou H">HO Raaschou</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cho, Kh" uniqKey="Cho K">KH Cho</name></author><author><name sortKey="Lee, Jy" uniqKey="Lee J">JY Lee</name></author><author><name sortKey="Lee, Kj" uniqKey="Lee K">KJ 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Phys Ther Sci</journal-id><journal-id journal-id-type="iso-abbrev">J Phys Ther Sci</journal-id><journal-id journal-id-type="publisher-id">JPTS</journal-id><journal-title-group><journal-title>Journal of Physical Therapy Science</journal-title></journal-title-group><issn pub-type="ppub">0915-5287</issn><issn pub-type="epub">2187-5626</issn><publisher><publisher-name>The Society of Physical Therapy Science</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26957758</article-id><article-id pub-id-type="pmc">4756004</article-id><article-id pub-id-type="publisher-id">jpra-2015-652</article-id><article-id pub-id-type="doi">10.1589/jpts.28.202</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>Views of physiatrists and physical therapists on the use of gait-training
robots for stroke patients</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Kang</surname><given-names>Chang Gu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chun</surname><given-names>Min Ho</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref rid="cor1" ref-type="corresp"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Chang</surname><given-names>Min Cheol</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Kim</surname><given-names>Won</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Hee Do</surname><given-names>Kyung</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><aff id="aff1"><label>1)</label> Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea</aff></contrib-group><author-notes><corresp id="cor1"><label>*</label>Corresponding author. Min Ho Chun, Department of Rehabilitation Medicine, Asan Medical Center,
University of Ulsan College of Medicine: 88 Olympic-Ro, 43-Gil, Songpa-gu, Seoul 138-736,
Republic of Korea. (E-mail: <email xlink:href="mhchun@amc.seoul.kr">mhchun@amc.seoul.kr</email>)</corresp></author-notes><pub-date pub-type="epub"><day>30</day><month>1</month><year>2016</year></pub-date><pub-date pub-type="ppub"><month>1</month><year>2016</year></pub-date><volume>28</volume><issue>1</issue><fpage>202</fpage><lpage>206</lpage><history><date date-type="received"><day>20</day><month>8</month><year>2015</year></date><date date-type="accepted"><day>16</day><month>10</month><year>2015</year></date></history><permissions><copyright-statement>2016©by the Society of Physical Therapy Science. Published by IPEC
Inc.</copyright-statement><copyright-year>2016</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><license-p>This is an open-access article distributed under the terms of the Creative
Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. </license-p></license></permissions><abstract><p>[Purpose] Gait-training robots have been developed for stroke patients with gait
disturbance. It is important to survey the views of physiatrists and physical therapists
on the characteristics of these devices during their development. [Subjects and Methods] A
total of 100 physiatrists and 100 physical therapists from 38 hospitals participated in
our questionnaire survey. [Results] The most common answers about the merits of
gait-training robots concern improving the treatment effects (28.5%), followed by
standardizing treatment (19%), motivating patients about treatment (17%), and improving
patients’ self-esteem (14%). The subacute period (1–3 months post-stroke onset) was most
often chosen as the ideal period (47.3%) for the use of these devices, and a functional
ambulation classification of 0–2 was the most selected response for the optimal patient
status (27%). The preferred model was the treadmill type (47.5%) over the overground
walking type (40%). The most favored commercial price was $50,000–$100,000 (38.3%). The
most selected optimal duration for robot-assisted gait therapy was 30–45 min (47%),
followed by 15–30 min (29%), 45–60 min (18%), ≥ 60 min (5%), and < 15 min (1%).
[Conclusion] Our study findings could guide the future designs of more effective
gait-training robots for stroke patients.</p></abstract><kwd-group><title>Key words</title><kwd>Robot</kwd><kwd>Gait</kwd><kwd>Stroke</kwd></kwd-group></article-meta></front><body><sec sec-type="intro" id="s1"><title>INTRODUCTION</title><p>Gait disturbance is one of the most disabling sequelae of stroke<xref rid="r1" ref-type="bibr">1</xref>, <xref rid="r2" ref-type="bibr">2</xref><sup>)</sup>, with a reported
prevalence of 54–80%<xref rid="r3" ref-type="bibr">3</xref>, <xref rid="r4" ref-type="bibr">4</xref><sup>)</sup>. Gait disturbance often leads to loss of independence, which has
profound effects on both the patients and the caregivers and results in higher health-care
costs<xref rid="r4" ref-type="bibr">4</xref>,<xref rid="r5" ref-type="bibr">5</xref>,<xref rid="r6" ref-type="bibr">6</xref>,<xref rid="r7" ref-type="bibr">7</xref><sup>)</sup>. The
management of gait disturbance is therefore essential for stroke rehabilitation. Several
traditional gait rehabilitation techniques have been developed to improve gait disturbance
in stroke patients, including neurophysiological techniques (e.g., the Bobath, Brunnström,
proprioceptive neuromuscular facilitation, Vojta, and Rood methods)<xref rid="r8" ref-type="bibr">8</xref><sup>)</sup> and motor learning techniques (e.g., the Perfetti, Carr and
Shepherd, conductive education, and sensory integration methods)<xref rid="r9" ref-type="bibr">9</xref><sup>)</sup>. However, these traditional methods are very physically
intensive for the therapists, making it difficult for these practitioners to continuously
provide gait training for stroke patients<xref rid="r10" ref-type="bibr">10</xref><sup>)</sup>.</p><p>Robotic technology has progressed rapidly in recent years, and several gait-training robots
have now been developed, including the Lokomat (Hocoma Inc., Volketswil, Switzerland) and
G-EO (Rha Technologies, Olten, Switzerland). These devices use a powered exoskeleton with a
treadmill (Lokomat) or powered footplates with programmable trajectories, or are analogous
to a robotic elliptical trainer (G-EO)<xref rid="r11" ref-type="bibr">11</xref><sup>)</sup>.
These devices have alleviated the physical workload for the therapists and have thus gained
popularity. Several studies have also suggested that gait-training robots can improve
locomotor recovery in stroke patients<xref rid="r12" ref-type="bibr">12</xref><sup>)</sup>.</p><p>To aid in the further development and refinement of gait-training robots, it is important
to survey the views of the physiatrists and physical therapists who might use these
machines, in terms of merit, indication, type, price, treatment duration, and design. In our
current study, these parameters were evaluated by means of a questionnaire survey.</p></sec><sec sec-type="methods" id="s2"><title>SUBJECTS AND METHODS</title><p>A questionnaire was developed to collect the opinions of physiatrists and physical
therapists on robot-assisted gait-training therapy. The questionnaire was divided into six
sections: merit of robot-assisted gait training, indications for gait-training robots, robot
type, treatment duration, price, and design of gait-training robots. On the basis of these
six categories, a total of 46 questions were developed. Most questions were either closed
ended (multiple choice), or based on a 10-point Likert scale that gauges the level of
agreement with or feelings on the importance of an item. A total of 100 physiatrists and 100
physical therapists from 38 hospitals participated in the survey from November 2013 to May
2014. A self-written survey method was used, either face-to-face or by mail. A software was
used to randomly enroll participants from the registry of physiatrists and physical
therapists at the Korean Academy of Rehabilitation Medicine. This study was exempted from
institutional review board (IRB) review requirements based on the guidelines of Asan Medical
Center (IRB no. S-2013-1301-0006).</p><p>The SPSS version 18.0.0 software package was used for statistical analyses, and p-values
< 0.05 were considered to indicate statistically significant differences. Questions such
as perspectives on the use of gait-training robots for stroke patients according to
rehabilitation experience parameters were analyzed by using Pearson’s chi-square test and
ANOVA.</p></sec><sec sec-type="results" id="s3"><title>RESULTS</title><p>A total of 106 physiatrists and 107 physical therapists were invited to complete our study
survey. Six physiatrists and seven physical therapists could not complete the survey for
various reasons, resulting in a 94% participation rate. For 62 (31%) participants, the
surveys were conducted face-to-face. The remaining participants (69%) completed the survey
by mail. Concerning the experience of the study participants in providing rehabilitation to
stroke survivors, 33% had worked with stroke survivors for > 5 years, whereas 67% of
these practitioners had worked with such patients for 1–5 years. Sixty-seven (33.5%)
participants had a previous experience with using a rehabilitation robot. The survey
questions asked about the perception of the treatment effects of gait-training robots by
using a 10-point Likert scale for effectiveness. Participants with previous experience with
a gait-training robot selected a higher score in this regard. The average score of this
experienced group was 7.63, and that of the nonexperienced group was 6.93 (on a 10-point
Likert scale, p < 0.001; <xref rid="tbl_001" ref-type="table">Table 1</xref><table-wrap id="tbl_001" orientation="portrait" position="float"><label>Table 1.</label><caption><title>Perception of the treatment effects of a gait-training robot</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"></th><th align="center" rowspan="1" colspan="1">Experienced</th><th align="center" rowspan="1" colspan="1">Nonexperienced</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Treatment effect</td><td align="center" rowspan="1" colspan="1">7.63</td><td align="center" rowspan="1" colspan="1">6.93<sup>*</sup></td></tr></tbody></table><table-wrap-foot><p>Values are on a 10-point Likert scale. <sup>*</sup>p < 0.05 by using Fisher’s
exact test</p></table-wrap-foot></table-wrap>). A more detailed description of these statistics is provided in <xref rid="tbl_002" ref-type="table">Table 2</xref><table-wrap id="tbl_002" orientation="portrait" position="float"><label>Table 2.</label><caption><title>Baseline characteristics of the study participants</title></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"></th><th align="left" rowspan="1" colspan="1"></th><th align="center" rowspan="1" colspan="1">n (%)</th></tr></thead><tbody><tr><td align="left" rowspan="2" colspan="1">Gender</td><td align="left" rowspan="1" colspan="1">Male</td><td align="center" rowspan="1" colspan="1">137 (68.5)</td></tr><tr><td align="left" rowspan="1" colspan="1">Female</td><td align="center" rowspan="1" colspan="1">63 (31.5)</td></tr><tr><td align="left" rowspan="2" colspan="1">Occupation</td><td align="left" rowspan="1" colspan="1">Physiatrist</td><td align="center" rowspan="1" colspan="1">100 (50)</td></tr><tr><td align="left" rowspan="1" colspan="1">Physical therapist</td><td align="center" rowspan="1" colspan="1">100 (50)</td></tr><tr><td align="left" rowspan="2" colspan="1">Career</td><td align="left" rowspan="1" colspan="1">More than 5 years</td><td align="center" rowspan="1" colspan="1">56 (28)</td></tr><tr><td align="left" rowspan="1" colspan="1">Less than 5 years</td><td align="center" rowspan="1" colspan="1">144 (72)</td></tr><tr><td align="left" rowspan="2" colspan="1">Experienced in gait rehabilitation with a gait-training
robot</td><td align="left" rowspan="1" colspan="1">Experienced</td><td align="center" rowspan="1" colspan="1">67 (33.5)</td></tr><tr><td align="left" rowspan="1" colspan="1">Nonexperienced</td><td align="center" rowspan="1" colspan="1">133 (66.5)</td></tr><tr><td align="left" rowspan="4" colspan="1">Institution of employment</td><td align="left" rowspan="1" colspan="1">Tertiary hospital</td><td align="center" rowspan="1" colspan="1">63 (31.5)</td></tr><tr><td align="left" rowspan="1" colspan="1">General hospital</td><td align="center" rowspan="1" colspan="1">40 (20)</td></tr><tr><td align="left" rowspan="1" colspan="1">Rehabilitation hospital</td><td align="center" rowspan="1" colspan="1">72 (36)</td></tr><tr><td align="left" rowspan="1" colspan="1">Care hospital</td><td align="center" rowspan="1" colspan="1">25 (12.5)</td></tr></tbody></table></table-wrap>.</p><p>The survey included questions on the merit of robot-assisted gait training for stroke
patients. The most selected answer was “improvement of treatment effects” (28.5%), followed
by “standardized treatment” (19%), “triggering a patient’s motivation for treatment” (17%),
and “elevation of a patient’s self-esteem” (14%).</p><p>The survey also queried the appropriate period to use a gait-training robot. At “1–3 months
after stroke onset” (47.3%) was the most frequently selected answer, followed by “3–6 months
after stroke onset” (27.4%), “within 1 month after stroke onset” (14.4%), “6–12 months after
stroke onset” (8%), and “12 months after stroke onset” (3%).</p><p>The survey participants were also asked about the appropriate locomotor status of a patient
to warrant the application of a gait-training robot. Patient locomotor status was rated on
the basis of a functional ambulation classification (FAC) score from 0 to 5. The most
selected response for the optimal patient locomotor status was “FAC 1” (27%), followed by
“FAC 2” (24%), “FAC 0” (23%), “FAC 3” (11%), “FAC 4” (8%), and “FAC 5” (2%). The extent of
agreement with the application of a gait-training robot at each locomotor status by using a
10-point Likert scale was also surveyed. The average scores for each locomotor status
classification are reported in <xref rid="tbl_003" ref-type="table">Table
3</xref><table-wrap id="tbl_003" orientation="portrait" position="float"><label>Table 3.</label><caption><title>Average scores for each locomotor status</title></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"></th><th valign="top" align="center" rowspan="1" colspan="1">Mean</th><th valign="top" align="center" rowspan="1" colspan="1">SD</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 0</td><td align="center" valign="top" rowspan="1" colspan="1">6.34</td><td align="center" valign="top" rowspan="1" colspan="1">±2.37</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 1</td><td align="center" valign="top" rowspan="1" colspan="1">6.89</td><td align="center" valign="top" rowspan="1" colspan="1">±1.94</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 2</td><td align="center" valign="top" rowspan="1" colspan="1">6.25</td><td align="center" valign="top" rowspan="1" colspan="1">±2.10</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 3</td><td align="center" valign="top" rowspan="1" colspan="1">4.93</td><td align="center" valign="top" rowspan="1" colspan="1">±2.41</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 4</td><td align="center" valign="top" rowspan="1" colspan="1">4.43</td><td align="center" valign="top" rowspan="1" colspan="1">±2.44</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">FAC 5</td><td align="center" valign="top" rowspan="1" colspan="1">2.85</td><td align="center" valign="top" rowspan="1" colspan="1">±2.60</td></tr></tbody></table><table-wrap-foot><p>Values are on a 10-point Likert scale. FAC: functional ambulation classification</p></table-wrap-foot></table-wrap>, and the mean scores for each locomotor status score showed an identical
pattern.</p><p>The most useful type of gait-training robot for stroke patients was also surveyed. The most
selected answer was the “treadmill type” (47.5%), followed by the “overground walking type”
(40%), “foot plate-based gait trainer” (11.5%), and “fixed-gait trainer” (1%). The survey
also asked about the appropriate treatment duration for robot-assisted gait therapy. The
“30–45 min” period was selected most often (47%), followed by “15–30 min” (29%), “45–60 min”
(18%), “≥ 60 min” (5%), and “< 15 min” (1%).</p><p>When queried about the most suitable price for a gait-training robot for stroke patients,
the survey participants most frequently selected “from $50,000 to $100,000” (38.3%),
followed by “from $100,000 to $200,000” (37.8%), “below $50,000” (14.4%), and “from $200,000
to $500,000” (8.5%).</p><p>The participants were also surveyed about design considerations. Most of the respondents
selected “stability” (49%), followed by “comfort” (28%), “cutting edge” (13%),
“friendliness” (9%), and “other” (1%). When asked about the most important consideration for
the production of gait-training robots, the most frequently selected answer was “convenience
of on and off” (39.7%), followed by “fitting sense” (23.9%), “light weight” (23.2%),
“miniaturization” (10.2%), and “aesthetics” (3%). The most frequently selected response
concerning the intention detector sensors for gait-training robots was “EMG”
(electromyography) (25%), followed by “foot pressure” (22%), “joint torque” (20%), “tilt
sensor” (19%), and “other” (14%). When asked about the ideal graph type for training
results, “bar graph” (42.3%) was selected most often, followed by “line graph” (27.9%),
“radiant graph” (21.9%), and “pie graph” (8.0%). In terms of the most preferred materials
for direct contact areas, most respondents to the survey selected “silicon” (60%), followed
by “leather” (21%), “cloth” (13%), “wood” (4%), and “others” (2%).</p></sec><sec sec-type="discussion" id="s4"><title>DISCUSSION</title><p>In our present study, the views of physiatrists and physical therapists on the use of
gait-training robots were surveyed, specifically their views on the merits of robot-assisted
gait training; the indications for using these devices; the optimal treatment duration of
robot-assisted gait training; and the type, price, and design of gait-training robots.</p><p>The largest group of participants selected “treatment effect of gait training” as the
principal reason that would merit robot-assisted gait training, followed by standardized
treatment. This finding is consistent with those of previous reports showing that
robot-assisted gait training exhibited equivalent or better treatment effects compared with
conventional physical therapy<xref rid="r11" ref-type="bibr">11</xref>, <xref rid="r13" ref-type="bibr">13</xref>, <xref rid="r14" ref-type="bibr">14</xref><sup>)</sup>. In conventional gait training by physical therapists, the protocols
and intensities of this intervention can differ depending on the practitioner. By contrast,
robot-assisted gait training can provide reproducible symmetrical gait kinematic patterns of
leg movements<xref rid="r15" ref-type="bibr">15</xref><sup>)</sup>, and thus enable
standardized treatment to be continuously provided to stroke patients<xref rid="r16" ref-type="bibr">16</xref><sup>)</sup>.</p><p>In the survey of experts, the most appropriate patient locomotor status for the application
of gait-training robots was indicated to be an FAC score ranging from 0 to 2, which
corresponds to patients who cannot walk without assistance<xref rid="r17" ref-type="bibr">17</xref><sup>)</sup>. Conventional gait training thus imposes a significant physical
burden on therapists when treating patients with FAC scores of 0–2<xref rid="r18" ref-type="bibr">18</xref><sup>)</sup>, and this approach could not therefore be readily applied
on a large scale for such cases. Importantly, this limitation could be overcome by the
application of robot-assisted gait training<xref rid="r19" ref-type="bibr">19</xref><sup>)</sup>.</p><p>Our current study survey indicated that the most appropriate period to apply gait-training
robots in stroke patients is at 1–3 months after the disease onset. This result concurs with
previous reports of a significant improvement in walking speed and functional outcomes when
these devices are used during the acute post-stroke phase (2.5–14 weeks)<xref rid="r20" ref-type="bibr">20</xref>,<xref rid="r21" ref-type="bibr">21</xref>,<xref rid="r22" ref-type="bibr">22</xref>,<xref rid="r23" ref-type="bibr">23</xref><sup>)</sup>.
Additionally, several clinical studies have reported that recovery from stroke mainly occurs
during the first 3 months after its onset<xref rid="r24" ref-type="bibr">24</xref>,<xref rid="r25" ref-type="bibr">25</xref>,<xref rid="r26" ref-type="bibr">26</xref><sup>)</sup>.</p><p>Treadmill gait-training robots were the most selected type in our survey, followed by
overground walking-type robots. This finding was consistent with those of preliminary
studies that suggested that the use of treadmill gait training leads to a better recovery of
ambulation, with beneficial effects on overground walking speed and endurance, and a
reduction in the physical assistance required to walk<xref rid="r27" ref-type="bibr">27</xref>,<xref rid="r28" ref-type="bibr">28</xref>,<xref rid="r29" ref-type="bibr">29</xref><sup>)</sup>. Conversely, fewer of our survey participants selected a foot
plate–based gait trainer. This result was not surprising, as foot plate-based gait trainers
are considered to have several drawbacks compared with other types of gait-training robots.
First, foot plate–based gait trainers lack a true swing phase during the gait cycle<xref rid="r30" ref-type="bibr">30</xref><sup>)</sup>. Additionally, they cannot control
spasticity of the lower extremities<xref rid="r11" ref-type="bibr">11</xref><sup>)</sup> and
would be difficult to use for patients with a lower locomotor status (FAC 0–2).</p><p>The appropriate duration of a robot-assisted gait therapy session was considered to be
30–45 min by most of our survey respondents. This was also an unsurprising finding. A
previous study has suggested that > 30 min of gait training is effective in improving the
locomotor function of stroke patients<xref rid="r31" ref-type="bibr">31</xref><sup>)</sup>.
Moreover, > 30 min of aerobic exercise is required for an improved cardiovascular
function<xref rid="r32" ref-type="bibr">32</xref><sup>)</sup>. However, a prolonged
treatment duration can cause patients to experience physical fatigue and to lose interest in
continuing the therapy<xref rid="r33" ref-type="bibr">33</xref>, <xref rid="r34" ref-type="bibr">34</xref><sup>)</sup>.</p><p>Concerning the suitable price for a gait-training robot, the most commonly selected
response was the $50,000–$100,000 range, followed by $100,000–$200,000. The most important
requirements to consider in the design of a gait-training robot were considered by our
survey participants to be (in order of importance): stability, comfort, cutting-edge
features, and friendliness. Bar graphs were the most preferred graphical representations of
real-time training states and training results, perhaps because these data are easier to
comprehend. An EMG sensor was considered to be the most appropriate type for the detection
of intended gait initiation. From these responses, we contend that gait-training robot
producers should consider the results of our survey when designing and then manufacturing
gait-training robots.</p><p>To the best of our knowledge, only one previous study has investigated the views of
therapists on the optimal practices and design requirements for the development of an
upper-limb stroke rehabilitation robot<xref rid="r35" ref-type="bibr">35</xref><sup>)</sup>.
However, no study to date has surveyed the views of practitioners on their use of
gait-training robots. Our current study is the first to investigate the actual user demands
for gait-training robots, and to evaluate the perspectives on the use of these devices to
help in the rehabilitation of stroke patients. A notable limitation of our current
investigation was that only one-third of the participants had experience with robot-assisted
gait training. Future studies should therefore enroll more participants with such an
experience.</p></sec></body><back><ack><p>This study was partially supported by the research and development program of the Ministry
of Trade, Industry, and Energy/Korea Evaluation Institute of Industrial Technology
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<pub-id pub-id-type="pmid">21184626</pub-id></mixed-citation></ref></ref-list></back></pmc><affiliations><list></list><tree><noCountry><name sortKey="Chang, Min Cheol" sort="Chang, Min Cheol" uniqKey="Chang M" first="Min Cheol" last="Chang">Min Cheol Chang</name><name sortKey="Chun, Min Ho" sort="Chun, Min Ho" uniqKey="Chun M" first="Min Ho" last="Chun">Min Ho Chun</name><name sortKey="Hee Do, Kyung" sort="Hee Do, Kyung" uniqKey="Hee Do K" first="Kyung" last="Hee Do">Kyung Hee Do</name><name sortKey="Kang, Chang Gu" sort="Kang, Chang Gu" uniqKey="Kang C" first="Chang Gu" last="Kang">Chang Gu Kang</name><name sortKey="Kim, Won" sort="Kim, Won" uniqKey="Kim W" first="Won" last="Kim">Won Kim</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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