16

AIntroduction

cute phase rehabilitation for stroke patients has beenactively performed in recent years. Early post-stroke

rehabilitation programs can effectively improve physicalfunction and the ability to perform activities of dailyliving (ADLs) in patients and make early hospitaldischarge possible.1 In Japan, patients with mild strokeare usually discharged home within a short period oftime after the event, although a substantial number of

　Original Contribution Kitasato Med J 2018; 48: 16-25　

Gait speed and balance function strongly determine the ability towalk independently without using a wheelchair in

a facility setting for stroke patients

Yuta Ichinosawa,1 Shinobu Shimizu,2 Nami Takemura,3 Katsuya Taira,3

Michiru Hamakawa,3 Yuki Nakachi,3 Morihiko Goya,3 Tomoya Yamazato,3

Ryota Shimose,1 Hajime Miura,1 Satoshi Tsunoda,1 Ryuichiro Yamada,1

Takayuki Nakanishi,3 Atsuhiko Matsunaga1,2

1 Kitasato University Graduate School of Medical Sciences2 Kitasato University, School of Allied Health Sciences3 Okinawa Rehabilitation Center Hospital, Okinawa

Objective: There appears to be a gap between ambulatory ability and actual walking performance instroke patients in facility settings. Few studies have reported on indicators that can accurately assesspatient ability to walk independently in facility settings without wheelchair assistance (practicalambulation). This study aimed to examine whether or not measures of physical function can determinepractical ambulation status in stroke patients admitted to a convalescent rehabilitation ward.Methods: A total of 443 stroke patients who received inpatient rehabilitation services were enrolled inthis cross-sectional study. In addition to clinical characteristics including cognitive function, lowerlimb motor function on affected and unaffected sides (Stroke Impairment Assessment Set and legstrength, respectively), the Berg Balance Scale (BBS), comfortable gait speed (CGS), and walkingability were examined in patients who were divided into three groups according to time after strokeonset (early, middle, and late phases). Multivariate logistic regression and receiver operatingcharacteristic curve analyses were performed to examine whether these indicators can discriminatebetween non-practical and practical walkers after adjusting for clinical characteristics in each group.Results: Affected lower limb function in the early- and middle-phase groups (P < 0.001), and BBS andCGS in all groups (P < 0.001), were significantly associated with practical ambulation. Cut-off pointsof BBS and CGS for discriminating between non-practical and practical walkers were approximately45 points and 30 m/minute, respectively, in all groups.Conclusion: BBS and CGS are useful determinants of practical ambulation in stroke patients admittedto a convalescent rehabilitation facility.

Key words: stroke, walking ability, balance, gait speed

patients with moderate to severe stroke, especially thosein need of intensive rehabilitation, are admitted tospecialized wards for rehabilitation (i.e., convalescentrehabilitation ward), where they stay for several monthseven after completing an acute stroke rehabilitationprogram.1 In convalescent rehabilitation wards, patientsreceive seamless interventions not only in a rehabilitationtraining room but also in their living spaces and undergorehabilitation programs that focus on improving ADLability.1 Because recovery of ambulation is one of the

Received 20 November 2017, accepted 4 December 2017Correspondence to: Atsuhiko Matsunaga, Department of Rehabilitation Sciences, Graduate School of Medical Sciences, Kitasato University1-15-1 Kitasato, Minami-ku, Sagamihara, Kanagawa 252-0373, JapanE-mail: atsuhikonet@gmail.com

17

Physical function for practical ambulation

most important goals for post-stroke patients,2 accurateassessment of actual walking performance in facilitysettings is important.3

The Functional Independence Measure (FIM) andFunctional Ambulation Category (FAC)4-8 are mainlyused to assess walking ability in patients undergoingrehabilitation. FIM scores ≥6 indicate walkingindependence (6: "modified independence," 7: "completeindependence") in a hospital ward or facility.4 "Modifiedindependence" means the patient can walk a minimumof 150 ft (50 m) without supervision using assistivedevices such as a brace (orthosis) and cane.5 However,this assessment also applies to patients who are normallyambulant but use a wheelchair under certain circumstances,such as going to the toilet, albeit independently. Assuch, the FIM alone cannot discriminate between practicaland non-practical walking abilities in facility patients.On the other hand, the FAC is used to evaluate ambulationability on a 6-point scale based on the extent of physicalsupport patients require when walking.6,7 For example,patients who can only walk indoors are assessed as level2 ("household ambulation").8 However, temporarywheelchair use is not taken into consideration in thisassessment either.

The ability of patients to walk independently withoutwheelchair assistance around a hospital ward or facility,i.e., practical ambulation, is not taken into account whenassessing ambulation status; and, to our knowledge, nostudy has adequately examined indicators of practicalambulation in patients living in facilities. There is likelyto be a gap between ambulation ability and actualambulatory performance in stroke patients in hospitalwards or facilities.9-11

Walking training by therapists in a rehabilitationtraining room alone may not result in gains in physicalfitness due to the limited time inpatients spendparticipating in exercise training.12,13 Measures orindicators of practical ambulation in stroke patients infacility settings, such as in convalescent rehabilitationwards, need to be established, as they will help determineor predict practical ambulation status in patients, andhelp to develop concrete guidelines aimed at activelypromoting walking guidance in convalescentrehabilitation wards.

Moreover, few studies have reported on therelationship between functional impairment and disabilityin terms of phase or time after stroke although functionalrecovery appears to be influenced by time after stroke.14

Several previous reports have pointed out that the first 3months correspond to the period of greatest neuroplasticity,and changes in functional recovery decrease and gradually

reach a plateau beyond the 3-month period post-stroke.15

Therefore, this study aimed to examine whether ornot measures of physical function can determine practicalambulation status in stroke patients admitted to aconvalescent rehabilitation ward by time after stroke.

Materials and Methods

Participants were patients with stroke hemiparesis whounderwent inpatient rehabilitation therapy at the OkinawaRehabilitation Center Hospital from 2011 to 2016.Inclusion criteria were patients who were diagnosed withcerebral hemorrhage or cerebral infarction by computedtomography or magnetic resonance imaging, able to walk10 m with or without assistive devices, and providedinformed consent. Patients who were diagnosed withsubarachnoid hemorrhage or brain tumor, had significantmusculoskeletal or neurological disorders unrelated tostroke (e.g., multiple sclerosis or Parkinson's disease),orthopedic diseases (e.g., spinal canal stenosis, osteoarthritis,or spine or leg fractures), and visual deficits, or haddifficulty understanding instructions were excluded fromthe study.

Participants underwent a rehabilitation programconsisting of physical therapy, occupational therapy, andspeech therapy on a daily basis, with the goal of achieving9 units per day (1 unit comprised a 20-min session) inaccordance with the 2014 Japanese Guidelines for theManagement of Stroke.16 Physical therapy involvedperforming standing up, sitting, and standing motions indaily living, transfer motions, wheelchair driving,walking, and stair climbing and descending practice in astepwise manner. Occupational therapy includedexercises involving ADLs, such as toilet activities,grooming, dressing, and bathing activities, upperextremity functional exercises including switching thedominant hand, and evaluation and training of higherbrain dysfunctions. In speech therapy, rehabilitationtargeted improving aphasia, dysarthria, eating disorders,and dysphagia.17,18 All the patients agreed to participatein the follow-up study.

This study was approved by the Okinawa RehabilitationCenter Hospital Research Ethics Committee and wasconducted in accordance with the Declaration of Helsinki.

Patient clinical characteristicsClinical characteristics including age, sex, body weight,stroke type, affected side, and time from stroke onsetwere collected from patient medical records. In addition,cognitive function was assessed using the cognitivesubscale of the Functional Independence Measure (Cog

18

Ichinosawa Y. et al.

FIM). The Cog FIM subscale is a part of the globalFIM assessment and includes two items (communicationand social cognition) that relate to cognitive functionssuch as comprehension, expression, social interaction,problem-solving, and memory.19 A score of 35 pointsrepresents optimal performance. A significant positivecorrelation has been found with the Cog FIM and Mini-Mental State Examination.20

Physical function

Affected lower limb functionAffected lower limb function was assessed with the motoritems of the Stroke Impairment Assessment Set (SIAS)for neurological impairment.21 The SIAS, developed forstroke outcome research in Japan,22 assesses variousaspects of impairment in hemiplegic patients, and hasbeen shown to demonstrate interrater reliability,predictive validity, sensitivity, and scale quality.23 TheSIAS motor subscore of the lower limb ranges from 0 to15 points21 and consists of scores from three tests for thelower extremity (hip-flexion, knee-extension, and foot-pat); each score ranges from 0 to 5 points: 0 indicatingcomplete paralysis, 3 indicating an ability to completethe task with difficulty, and 5 indicating no paresis.

Unaffected lower limb functionUnaffected lower limb function was assessed bymeasuring maximum leg strength with a hand-helddynamometer (μTas F1; ANIMA, Tokyo, Japan). Theaccuracy and reliability of this instrument have beenreported previously.24,25 Patients were seated on a bed inan upright posture with their feet over the side of the bedand their hip and knee flexed at an angle of 90 degrees.The dynamometer pad was placed perpendicularly to theleg just above the malleoli of the ankle. Patients weretold to push against the dynamometer pad by attemptingto straighten their knees for a period of 5 seconds.Isometric knee extensor strength was measured twice,and the highest value was used as the maximum legstrength. To adjust for differences in physical constitutionamong patients, maximum isometric leg strength wasdivided by body weight and expressed as a percentage ofbody weight.26

Balance functionBalance function was assessed using the Berg BalanceScale (BBS), a physical performance measure comprisesof 14 items. Each item rates the ability of participants tomaintain stability in a specified functional task on a 5-point (0−4) scale.27 Possible total scores range from 0

to 56, with greater scores indicating better balance. BBShas been demonstrated to show excellent test-retestreliability and inter-rater reliability for participants withstroke.28-30

Gait speedSelf-selected comfortable gait speed (CGS) along a 10-meter walkway was measured to assess patient walkingability. Participants were instructed to "walk at acomfortable self-selected pace." Participants walked atotal of 14 meters, and the time was measured for 10meters excluding the first and last 2 meters. Using adigital stopwatch that records time within 0.01 sec, timemeasurement was manually started when the 0-metermark was crossed, and stopped when the 10-meter markwas crossed. Self-selected CGS was measured twiceand the average value (m/minute) was used to representwalking ability. Participants were allowed to use thewalking aid and/or orthotic device they normally usedthroughout the measurements. Self-selected CGSmeasured in seconds or minutes is a valid and reliablemeasure of walking ability in people who have sufferedstroke.31,32

Practical ambulationPractical ambulation was defined as the ability to walkindependently to all places in a hospital ward without awheelchair or supervision, and corresponded to "walkingindependence" according to the FIM. Patients with anFIM score ≥6 (6: "modified independence", 7: "completeindependence") were observed for 1 week to determinewhether they actually used a wheelchair in the hospitalward, and those who did not use a wheelchair in thehospital ward were defined as practical walkers, i.e., thosewho were ambulant without wheelchair assistance at leastwithin the hospital ward. Conversely, those with an FIMscore ≥6 who used a wheelchair in the hospital ward orhad an FIM score ≤5 (i.e., requiring a wheelchair in thehospital ward) were defined as non-practical walkers. Aphysiotherapist or nurse in the hospital ward made theobservations.

Statistical analysisData are expressed as mean ± standard deviation (SD)or number (%). Enrolled patients were divided into thefollowing 3 groups according to time after stroke onset:the early-phase group (1−2 months after onset), themiddle-phase group (3−4 months after onset), and thelate-phase group (5−6 months after onset). In addition,patients in each group were classified as non-practical orpractical walkers, as described above. Differences in

19

Physical function for practical ambulation

mean age, time since stroke, Cog FIM, SIAS of thelower limb, leg strength, BBS, and CGS between non-practical and practical walkers were assessed in eachgroup by ANOVA (analysis of variance). The χ2 test fortrend was used to assess dose-response relations forvariables (i.e., sex, stroke type, and affected side) betweennon-practical and practical walkers. Correlations betweenSIAS of the lower limb, leg strength, BBS, and CGS inboth non-practical and practical walkers were analyzedby calculating Spearman's rank correlation coefficients.To minimize the effect of collinearity and avoidredundancy, a correlation matrix was developed with acut-off value of 0.7,33 and when performing multivariateanalysis, variables having a correlation coefficient witha cut-off value of ≥0.7 were analyzed separately.Univariate and multivariate logistic regression analyseswere performed to examine associations between physicalfunction measures and practical ambulation afteradjusting for clinical characteristics. The area under thereceiver operating characteristic (ROC) curve (AUC) wasestimated for each significant variable identified. If theAUC was significant, the point that yielded the highestcombined sensitivity and specificity was used as a cut-off point to estimate accuracy and positive and negativepredictive values.

Statistical significance was defined as P < 0.05. Allstatistical analyses were performed using a computerizedStatistical Package for Social Sciences (IBM SPSS statics21.0 for Mac; IBM Corp., Armonk, NY, USA).

Results

Patient characteristics and physical functionA total of 443 stroke patients met the inclusion criteria.The clinical characteristics and physical function of theearly-, middle-, and late-phase groups are summarizedin Table 1. The early-phase group was comprised of 165participants including 91 non-practical walkers and 74practical walkers, the middle-phase group was comprisedof 167 participants including 78 non-practical walkersand 89 practical walkers, and the late-phase group wascomprised of 111 participants including 45 non-practicalwalkers and 66 practical walkers.

Cognitive function according to Cog FIM was higherin practical walkers than in non-practical walkers in all 3groups (P < 0.01). SIAS of the lower limb, leg strength,BBS, and CGS were also significantly higher in practicalwalkers than in non-practical walkers in all 3 groups (P <0.05).

Correlation matrixSpearman's rank correlation coefficients for SIAS of thelower limb, leg strength, BBS, and CGS are shown inTable 2. SIAS of the lower limb was significantlycorrelated with leg strength (P < 0.05) only in the early-phase group, and with BBS and CGS in all 3 groups (P <0.01). Leg strength was significantly correlated withBBS and CGS in all 3 groups (P < 0.01). A significantcorrelation was also observed between CGS and BBS inall 3 groups (P < 0.01).

Univariate and multivariate logistic regression analysesThe results of univariate and multivariate logisticregression analyses for determinants of practicalambulation are shown in Table 3. The correlationcoefficient between BBS and CGS, calculated tominimize the effect of multicollinearity, showed a cut-off value of ≥0.7. Therefore, when performingmultivariate analysis, BBS and CGS were analyzedseparately.

In the early-phase group, multivariate logisticregression analysis revealed that SIAS of the lower limb(P = 0.005) and BBS (P < 0.001) were significantlyassociated with practical ambulation in the BBS model,whereas SIAS of the lower limb (P = 0.031) and CGS (P< 0.001) were significantly associated with practicalambulation in the CGS model.

In the middle-phase group, multivariate logisticregression analysis revealed that SIAS of the lower limb(P = 0.004) and BBS (P < 0.001) were significantlyassociated with practical ambulation in the BBS model,whereas Cog FIM (P = 0.032), SIAS of the lower limb(P = 0.027), and CGS (P < 0.001) were significantlyassociated with practical ambulation in the CGS model.

In the late-phase group, multivariate logistic regressionanalysis revealed that BBS (P = 0.004) was significantlyassociated with practical ambulation in the BBS model,whereas time since stroke (P = 0.024), Cog FIM (P =0.024) and CGS (P < 0.001) were significantly associatedwith practical ambulation in the CGS model.

ROC analysisROC curves were generated for each variable as shownin Figure 1. In the early-phase group, the AUC for BBSwas 0.930 (P < 0.001), with a cut-off point of 44.5. TheAUC for CGS was 0.955 (P < 0.001), with a cut-off pointof 37.1 m/minute. In the middle-phase group, the AUCfor BBS was 0.915 (P < 0.001), with a cut-off point of49.5. The AUC for CGS was 0.921 (P < 0.001), with acut-off point of 33.5 m/minute. In the late-phase group,

20

the AUC for BBS was 0.868 (P < 0.001), with a cut-offpoint of 45.5. The AUC for CGS was 0.890 (P < 0.001),with a cut-off point of 26.3 m/minute.

Discussion

To our knowledge, this is the first study to report indicatorsthat can accurately assess the ability of patients to walkindependently in facility settings without wheelchairassistance in terms of phase after stroke. Recovery phaserehabilitation focuses on improving the ability of patientsto perform ADLs, including walking ability.1 However,ADL ability in a controlled environment, such as in arehabilitation training room, may not be fully utilized inactual life situations in facility settings. In clinicalsituations, even though patients are judged to have

independent walking ability in a rehabilitation setting,there may be occasions where they use wheelchairs incertain situations such as going to the toilet or the diningroom. It is easy to envision a substantial difference inwalking time and/or distance when comparing patientswho use wheelchairs and those who do not when movingaround the ward. Therefore, it is highly likely thatphysical activity after stroke affects improvements inwalking ability. An early, supported discharge trainingprogram, in which family members or rehabilitation staffactively support patients to carry out physical activitiesof daily living, is reportedly more closely associated withimproved physical function than conventional trainingin a rehabilitation room alone.12,13 In other words, patientsshould have increased opportunities for walking not onlyin a rehabilitation training room but also in their actual

Table 1. Characteristics and physical function of non-practical and practical walkers

Early-phase group Middle-phase group Late-phase group

Non-practical Practical Non-practical Practical Non-practical Practicalwalkers (n = 91) walkers (n = 74) walkers (n = 78) walkers (n = 89) walkers (n = 45) walkers (n = 66)

Age (yrs) 66.0 ± 14.1 63.1 ± 14.6 64.5 ± 14.2 63.3 ± 13.8* 64.8 ± 15.0 61.1 ± 13.8*Sex (Male/Female) 60/31 50/24 51/27 61/28 32/13 45/21Stroke type (Hemorrhage/Infarction) 39/52 29/45 45/33 39/50 31/14 29/37*Affected side (Right/Left) 48/43 41/33 41/37 45/44 24/21 42/24Time since stroke (months) 1.9 ± 0.3 1.9 ± 0.3 3.7 ± 0.5 3.7 ± 0.5 5.7 ± 0.5 5.5 ± 0.5Cog FIM 27.6 ± 7.1 31.1 ± 5.2** 27.1 ± 7.1 30.7 ± 5.5** 25.6 ± 7.4 30.5 ± 5.9**SIAS of lower limb 9.6 ± 3.7 13.6 ± 1.5** 8.4 ± 4.0 12.8 ± 2.5** 7.7 ± 4.1 11.4 ± 3.9**Leg strength (%BW) 33.8 ± 14.5 45.5 ± 17.9** 33.7 ± 13.8 42.9 ± 15.6** 36.1 ± 15.5 45.1 ± 16.1*BBS 32.9 ± 13.3 51.6 ± 5.2** 34.7 ± 11.2 51.1 ± 7.1** 34.4 ± 12.2 49.6 ± 6.7**CGS (m/minute) 21.2 ± 11.4 57.8 ± 18.3** 18.2 ± 10.2 50.4 ± 21.4** 17.9 ± 10.2 44.1 ± 19.2**

Cog FIM, Cognitive Functional Independence Measure; SIAS, Stroke Impairment Assessment Set; BW, body weight; BBS, BergBalance Scale; CGS, comfortable gait speedEarly-phase: 1 to 2 months after stroke onset, middle-phase: 3 to 4 months after stroke onset, late-phase: 5 to 6 months after stroke onset*P < 0.05 (vs. non-practical walkers), **P < 0.01 (vs. non-practical walkers)

Table 2. Spearman's rank correlation coefficients between physical function measures

Early-phase group Middle-phase group Late-phase group

SIAS of SIAS of SIAS ofLeg strength BBS CGS Leg strength BBS CGS Leg strength BBS CGS

lower extremity lower extremity lower extremity

SIAS of lower limb 1.00 0.17* 0.57** 0.62** 1.00 0.07 0.48** 0.59** 1.00 0.02 0.51** 0.64**Leg strength 1.00 0.43** 0.47** 1.00* 0.40** 0.38** 1.00* 0.42** 0.29**BBS 1.00** 0.73** 1.00** 0.72** 1.00** 0.73**CGS 1.00** 1.00** 1.00**

SIAS, Stroke Impairment Assessment Set; BBS, Berg Balance Scale; CGS, comfortable gait speedEarly-phase: 1 to 2 months after stroke onset, middle-phase: 3 to 4 months after stroke onset, late-phase: 5 to 6 months after stroke onset*Significant correlation at P<0.05, **Significant correlation at P<0.01

Ichinosawa Y. et al.

21

Table 3. Univariate and multivariate logistic regression analyses for determinants of practical ambulation

Multivariable analysis Multivariable analysisUnivariable analysis

BBS model CGS modelEarly-phase group

OR 95% CI P value OR 95% CI P value OR 95% CI P value

Age 1 year 0.99 0.97−1.01 0.196 1.01 0.96−1.06 0.707 1.01 0.95−1.08 0.729Female (vs. male) − 1.08 0.56−2.07 0.825 1.46 0.44−4.90 0.539 2.16 0.49−9.43 0.307Infarction (vs. hemorrhage) − 0.86 0.46−1.61 0.634 0.49 0.16−1.52 0.216 0.77 0.19−3.19 0.720Left affected side (vs. right) − 1.11 0.60−2.06 0.733 0.62 0.21−1.84 0.391 0.26 0.06−1.12 0.071Time since stroke 1 month 0.97 0.40−2.40 0.951 1.52 0.30−7.68 0.613 1.56 0.27−8.93 0.616Cog FIM 1 1.10 1.04 −1.16 0.001 0.99 0.90−1.08 0.791 1.03 0.94−1.12 0.537SIAS of lower limb 1 1.85 1.52−2.27 <0.001 1.53 1.14−2.04 0.005 1.53 1.04−2.25 0.031Leg strength 1 % BW 1.05 1.02−1.07 <0.001 1.01 0.97−1.05 0.685 0.99 0.94−1.04 0.609BBS 1 1.30 1.20−1.41 <0.001 1.24 1.13−1.37 <0.001CGS 1 m/minute 1.17 1.12−1.23 <0.001 1.18 1.11−1.27 <0.001R2 0.723 0.809

Middle-phase group

Age 1 year 0.99 0.97−1.02 0.560 1.02 0.97−1.07 0.495 1.01 0.96−1.06 0.697Female (vs. male) − 1.15 0.60−2.20 0.665 2.45 0.71−8.56 0.155 1.51 0.43−5.26 0.516Infarction (vs. hemorrhage) − 0.57 0.31−1.06 0.075 0.57 0.20−1.58 0.277 0.78 0.26−2.32 0.657Left affected side (vs. right) − 0.92 0.50−1.70 0.796 1.02 0.36−2.86 0.972 1.33 0.48−3.71 0.590Time since stroke 1 month 0.94 0.48−1.86 0.866 2.87 0.85−9.70 0.089 2.35 0.67−8.20 0.181Cog FIM 1 1.10 1.04−1.16 0.001 1.07 0.97−1.17 0.168 1.11 1.01−1.21 0.032SIAS of lower limb 1 1.47 1.30−1.67 <0.001 1.31 1.09−1.58 0.004 1.23 1.02−1.49 0.027Leg strength 1% BW 1.04 1.02−1.07 <0.001 1.02 0.98−1.07 0.371 1.02 0.98−1.07 0.380BBS 1 1.23 1.16−1.31 <0.001 1.17 1.08−1.27 <0.001CGS 1 m/minute 1.14 1.10−1.19 <0.001 1.11 1.06−1.16 <0.001R2 0.674 0.698

Late-phase group

Age 1 year 0.98 0.96−1.01 0.181 1.00 0.95−1.05 0.987 1.02 0.97−1.08 0.473Female (vs. male) − 0.87 0.38−1.99 0.742 2.80 0.54−14.44 0.219 2.37 0.38−14.78 0.357Infarction (vs. hemorrhage) − 0.35 0.16−0.79 0.011 0.38 0.12−1.25 0.112 0.41 0.11−1.52 0.180Left affected side (vs. right) − 1.53 0.71−3.31 0.279 1.37 0.39−4.81 0.621 1.57 0.40−6.15 0.518Time since stroke 1 month 0.46 0.21−1.03 0.058 0.33 0.09−1.18 0.087 0.18 0.04−0.80 0.024Cog FIM 1 1.12 1.05−1.19 0.001 1.09 0.98−1.22 0.097 1.14 1.02−1.28 0.024SIAS of lower limb 1 1.24 1.12−1.37 <0.001 1.09 0.92−1.30 0.315 0.98 0.80−1.19 0.832Leg strength 1% BW 1.04 1.01−1.07 0.006 1.01 0.97−1.06 0.572 1.03 0.98−1.08 0.287BBS 1 1.19 1.11−1.27 <0.001 1.14 1.04−1.24 0.004CGS 1 m/minute 1.13 1.08−1.18 <0.001 1.14 1.06−1.22 <0.001R2 0.575 0.646

OR, odds ratio; CI, confidence interval; Cog FIM, Cognitive Functional Independence Measure; SIAS, Stroke Impairment AssessmentSet; BBS, Berg Balance Scale; CGS, comfortable gait speed

Physical function for practical ambulation

22

AUC, area under the curve; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value; BBS, Berg BalanceScale; CGS, comfortable gait speed

Figure 1. Receiver operating characteristic curve analyses to assess dichotomized practical ambulation

A. Early-phase group

B. Middle-phase group

C. Late-phase group

Ichinosawa Y. et al.

23

living spaces.In the present study, the severity of paralysis in the

affected lower limb was a determinant of practicalambulation in a convalescent rehabilitation ward in theearly post-stroke phase. On the other hand, gait speedand balance function were consistently correlated withpractical ambulation in all the phases. Ambulation inreal-life settings demands concurrent motor and sensoryskills to deal with situations, such as the need to walkfaster, circumvent obstacles, and pay attention to what'sgoing on around oneself.34 Moving around the wardrequires not only the ability to walk, but also physicalfunction to walk without rest and quickly reach thedestination. In this context, the time required forlocomotion to a destination point is more important. Infact, our results showed that gait speed greatly affectedpractical ambulation in the hospital ward. Recent studieshave reported numerous factors related to communityambulation, including outdoor movement and socialparticipation; and gait speed was the most convenientindicator due to its relative simplicity, feasibility inmeasurement, and reduced psychological burden.35-38 Onthe other hand, balance has been reported to be a keycomponent that determines eventual ambulatory abilityafter stroke,39 and is also known to be closely related towalking ability, as well as gait speed.3,40,41

We examined cut-off values of gait speed and balanceas common determinants of practical ambulation in aconvalescent ward in post-stroke patients. Both gait speedand balance showed a high accuracy of approximately0.8. Cut-off values of CGS for determining practicalambulation at 1−2, 3−4, and 5−6 months after strokewere 37.1, 33.5, and 26.3 m/minute, respectively. Theseresults suggest that the shorter the time after stroke, thehigher the cut-off value of CGS for determining practicalambulation. The high CGS cut-off value in the earlypost-stroke phase could possibly be explained by lowerconfidence levels among stroke patients to move to placeswithout wheelchair assistance due to their lack ofexperience in walking post-stroke. In other words, evenif patients had sufficient walking ability (gait speed) forpractical ambulation, they may not be able to stop usinga wheelchair due to low confidence. There is also apossibility that physiotherapists or nurses in theconvalescent ward might have underestimated patients'walking abilities due to insufficient walking activitiesduring the early post-stroke phase.

These results suggest that stroke patients could achievepractical ambulation in a facility at a gait speed slowerthan 37.1 m/minute, which became the cut-off value ofCGS for determining practical ambulation at 1−2 months

after stroke, if they have more experience in walkingduring the early post-stroke phase. On the other hand,cut-off values of BBS for determining practicalambulation were roughly 45 points in all 3 phases. Theseresults are consistent with previous studies reportingsimilar cut-off values for BBS in post-stroke42 and healthyelderly people43,44 to walk safely without a falling support.

Friedman45 reported a link between the presence ofcognitive disorders and recovery of walking ability instroke patients. In the present study, Cog FIM wasfound to determine practical ambulation in the CGSmodel, but not in the BBS model. This could be due todifferences in the measuring methods between CGS andBBS. Higher cognitive function may be required in themeasurement of BBS than for CGS, since the formerinvolves many tasks under different conditions that cannotbe implemented if the patient cannot sufficientlyunderstand the tasks. Oppewal46 pointed out that patientswere not able to complete all 14 items of the BBS, largelydue to limited cognitive ability, even if patients hadsufficient. Therefore, Cog FIM might not have beenidentified as a determinant in the BBS model due to itsclose relationship with BBS.

The present study has several limitations. First, dueto the cross-sectional design, causal relationships couldnot be established between determinants of practicalambulation. Second, this study was carried out at a singlecenter with a relatively small number of patients. Third,although the possibility that physical activity after strokemay affect improvements in walking ability, data wasnot collected on patients' levels of physical activity in thehospital ward. Further investigation focusing on therelationship between changes in gait speed, balance, andwalking ability, in addition to physical activity levels ina convalescent rehabilitation ward, should be examinedlongitudinally in post-stroke patients.

The severity of paralysis in the affected lower limb(s)was found to be a determinant of practical ambulation ina facility setting during the early and the middle post-stroke phase. On the other hand, gait speed and balancefunction consistently discriminated between practical andnon-practical walkers in all the phases. Moreover, valuesof CGS ≥26 m/minute and BBS ≥45 points could provideuseful measures for determining practical ambulationstatus in post-stroke patients in facility settings. Ourfindings will help develop concrete guidelines aimed atactively promoting walking guidance in convalescentrehabilitation wards for stroke patients.

Physical function for practical ambulation

24

References

1. Mutai H, Furukawa T, Araki K, et al. Factorsassociated with functional recovery and homedischarge in stroke patients admitted to a convalescentrehabilitation ward. Geriatr Gerontol Int 2012; 12:215-22.

2. Masiero S, Avesani R, Armani M, et al. Predictivefactors for ambulation in stroke patients in therehabilitation setting: a multivariate analysis. ClinNeurol Neurosurg 2007; 109: 763-9.

3. Makizako H, Kabe N, Takano A, et al. Use of theBerg Balance Scale to predict independent gait afterstroke: a study of an inpatient population in Japan.PMR 2015; 7: 392-9.

4. Latham NK, Jette DU, Slavin M, et al. Physicaltherapy during stroke rehabilitation for people withdifferent walking abilities. Arch Phys Med Rehabil2005; 86: 41-50.

5. Patel AT, Duncan PW, Lai SM, et al. The relationbetween impairments and functional outcomespoststroke. Arch Phys Med Rehabil 2000; 81: 1357-63.

6. Holden MK, Gill KM, Magliozzi MR, et al. Clinicalgait assessment in the neurologically impaired.Reliability and meaningfulness. Phys Ther 1984;64: 35-40.

7. Mehrholz J, Wagner K, Rutte K, et al. Predictivevalidity and responsiveness of the functionalambulation category in hemiparetic patients afterstroke. Arch Phys Med Rehabil 2007; 88: 1314-9.

8. Viosca E, Martínez JL, Almagro PL, et al. Proposaland validation of a new functional ambulationclassification scale for clinical use. Arch Phys MedRehabil 2005; 86: 1234-8.

9. Fujita T, Sato A, Yamamoto Y, et al. Propensity-matched analysis of the gap between capacity andactual performance of dressing in patients with stroke.J Phys Ther Sci 2016; 28: 1883-7.

10. Fujita T, Sato A, Yamamoto Y, et al. Simple indicatorto judge the independence level required in dressingin a hospital ward for patients with stroke. J StrokeCerebrovasc Dis 2016; 25: 1838-42.

11. Iwai N, Aoyagi Y, Tokuhisa K, et al. The Gapsbetween capability ADL and performance ADL ofstroke patients in a convalescent rehabilitation ward.J Phys Ther Sci 2011; 23: 333-8.

12. Langhorne P, Taylor G, Murray G, et al. Earlysupported discharge services for stroke patients: ameta-analysis of individual patients' data. Lancet2005; 365: 501-6.

13. Cooke EV, Mares K, Clark A, et al. The effects ofincreased dose of exercise-based therapies to enhancemotor recovery after stroke: a systematic review andmeta-analysis. BMC Med 2010; 8: 60.

14. Gresham GE. Stroke outcome research. Stroke 1986;17: 358-62.

15. Newman M. The process of recovery afterhemiplegia. Stroke 1972; 3: 702-10.

16. The Japan Stroke Society. Guidelines forrehabilitation in the acute phase after stroke. In: TheJapan Stroke Society, ed. Japanese Guidelines forthe Management of Stroke 2015. Tokyo: KyowaKikaku, 2015: S270-318 (in Japanese).

17. Hirano Y, Maeshima S, Osawa A, et al. The effectof voluntary training with family participation onearly home discharge in patients with severe strokeat a convalescent rehabilitation ward. Eur Neurol2012; 68: 221-8.

18. Hirano Y, Nitta O, Hayashi T, et al. Development ofa prognostic scale for severely hemiplegic strokepatients in a rehabilitation hospital. Clin NeurolNeurosurg 2017; 158: 108-13.

19. Patil M, Gupta A, Khanna M, et al. Cognitive andfunct ional outcomes fol lowing inpat ientrehabilitation in patients with acquired brain injury:a prospective follow-up study. J Neurosci RuralPract 2017; 8: 357-63.

20. Heruti RJ, Lusky A, Barell V, et al. Cognitive statusat admission: does it affect the rehabilitation outcomeof elderly patients with hip fracture? Arch Phys MedRehabil 1999; 80: 432-6.

21. Chino N, Melvin JL. Functional Evaluation of StrokePatients. New York: Springer; 1996: 19-31.

22. Chino N, Sonoda S, Domen K, et al. Strokeimpairement assessment set. Jpn J Rehabil Med 1994;31: 119-25.

23. Tsuji T, Liu M, Sonoda S, et al. The strokeimpairment assessment set: its internal consistencyand predictive validity. Arch Phys Med Rehabil 2000;81: 863-8.

24. Suzuki M, Kirimoto H, Inamura A, et al. Therelationship between knee extension strength andlower extremity functions in nursing home residentswith dementia. Disabil Rehabil 2012; 34: 202-9.

25. Suzuki M, Yamada S, Inamura A, et al. Reliabilityand validity of measurements of knee extensionstrength obtained from nursing home residents withdimentia. Am J Phys Med Rehabil 2009; 88: 924-33.

26. Bohannon RW. Reference values for extremitymuscle strength obtained by hand-held dynamometryfrom adults aged 20 to 79 years. Arch Phys MedRehabil 1997; 78: 26-32.

27. Berg K, Wood-Dauphinee S, Williams JI. TheBalance Scale: reliability assessment with elderlyresidents and patients with an acute stroke. Scand JRehabil Med 1995; 27: 27-36.

Ichinosawa Y. et al.

25

28. Hiengkaew V, Jitaree K, Chaiyawat P. Minimaldetectable change of the Berg Balance Scale, Fugl-Meyer Assessment Scale, Timed "Up & Go" Test,gait speeds, and 2-minute walk test in indivisualswith chronic stroke with different degrees of ankleplanterflexor tone. Arch Phys Med Rehabil 2012;93: 1201-8.

29. Liston RA, Brouwer BJ. Reliability and validity ofmeasuras obtained from stroke patients using theBalanse Master. Arch Phys Med Rehabil 1996; 77:425-30.

30. Mao HF, Hsueh IP, Tang PF, et al. Analysis andcomparison of the psychometric properties of threebalance measures for stroke patients. Stroke 2002;3: 1022-7.

31. Van Hedel HJ, Wirz M, Dietz V. Assessing walkingability in subjects with spinal cord injuary: validityand reliability of 3 walking tests. Arch Phys MedRehabil 2005; 86: 190-6.

32. Collen FM, Wade DT, Bradshaw CM. Mobility afterstroke: reliability of measures of impairment anddisability. Int Disabil Stud 1990; 12: 6-9.

33. Veerbeek JM, Van Wegen EE, Harmeling-Van derWel BC, et al. Is accurate prediction of gait innonambulatory stroke patients possible within 72hours poststroke? The EPOS study. NeurorehabilNeural Repair 2011; 25: 268-74.

34. Eng JJ, Tang PF. Gait training strategies to optimizewalking ability in people with stroke: a synthesis ofthe evidence. Expert Rev Neurother 2007; 7: 1417-36.

35. Bijleveld-Uitman M, van de Port I, Kwakkel G. Isgait velocity or walking distance a better predictorfor community walking after stroke? J Rehabil Med2013; 45: 535-40.

36. Lord SE, McPherson K, McNaughton HK, et al.Community ambulation after stroke: how importantand obtainable is it and what measures appearpredictive? Arch Phys Med Rehabil 2004; 85: 234-9.

37. Rosa MC, Marques A, Demain S, et al. Fast gaitspeed and self-perceived balance as valid predictorsand discriminators of independent communitywalking at 6 months post-stroke--a preliminary study.Disabil Rehabil 2015; 37: 129-34.

38. An S, Lee Y, Shin H, et al. Gait velocity and walkingdistance to predict community walking after stroke.Nurs Health Sci 2015; 17: 533-8.

39. Kollen B, van de Port I, Lindeman E, et al. Predictingimprovement in gait after stroke: a longitudinalprospective study. Stroke 2005; 36: 2676-80.

40. Bland MD, Sturmoski A, Whitson M, et al. Predictionof discharge walking ability from initial assessmentin a stroke inpatient rehabilitation facility population.Arch Phys Med Rehabil 2012; 93: 1441-7.

41. Torkia C, Best KL, Miller WC, et al. Balanceconfidence: a predictor of perceived physicalfunction, perceived mobility, and perceived recovery1 year after inpatient stroke rehabilitation. Arch PhysMed Rehabil 2016; 97: 1064-71.

42. Maeda N, Kato J, Shimada T. Predicting theprobability for fall incidence in stroke patients usingthe Berg Balance Scale. J Int Med Res 2009; 37:697-704.

43. Shumway-Cook A, Baldwin M, Polissar NL, et al.Predicting the probability for falls in community-dwelling older adults. Phys Ther 1997; 77: 812-9.

44. Jeon YJ, Kim GM. Comparison of the Berg BalanceScale and Fullerton Advanced Balance Scale topredict falls in community-dwelling adults. J PhysTher Sci 2017; 29: 232-4.

45. Friedman PJ. Gait recovery after hemiplegic stroke.Int Disabil Stud 1990; 12: 119-22.

46. Oppewal A, Hilgenkamp TI, van Wijck R, et al.Feasibility and outcomes of the Berg Balance Scalein older adults with intellectual disabilities. Res DevDisabil 2013; 34: 2743-52.

Physical function for practical ambulation