Research PaperOpen Access

The effect of physical therapy with goal attainment scaling on gait function in patients with subacute stroke

Department of Physical Therapy, Graduate School of Health and Welfare, Eulji University, Uijeongbu Campus, Gyeonggi 11759, Republic of Korea

Rehabilitation Medicine Team, CHA Bundang Medical Center, Seongnam, Gyeonggi 13496, Republic of Korea

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Min-Hee Kim

https://orcid.org/0000-0001-6072-9459

Department of Physical Therapy, College of Health Sciences, Eulji University, Seongnam Campus, Gyeonggi 13135, Republic of Korea

E-mail Address: kmh12@eulji.ac.kr

E-mail Address: minxi1004@gmail.com

Corresponding author.

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https://doi.org/10.1142/S1013702525500015Cited by:0 (Source: Crossref)

Abstract

Background: Correct goal setting for patients is one of the most important aspects of physical therapy.

Objective: The purpose of this study was to investigate the effect of physical therapy using goal attainment scaling (GAS) scores to assess the gait function in patients with subacute stroke and related factors.

Methods: This retrospective cohort study was conducted using the medical records of 129 patients with subacute stroke who had been treated with intensive rehabilitation intervention. This study was approved by the Institutional Review Board of Bundang Cha Hospital (2021-06-008). The functional ambulation category (FAC) was used to set goals with the involvement of patients and their caregivers after the initial assessment, and raw GAS scores were calculated according to whether the goals were achieved through assessment one month later based on the FAC score. The groups were then divided according to the raw GAS scores (−1, 0, 1, or 2), and the general characteristics and clinical assessment scores were statistically analysed.

Results: From our results, there were differences in clinical assessment scores based on raw scores on the GAS ( $p < 0.05$ ) and correlation between raw scores on the GAS and improvement scores on the clinical assessment items ( $p < 0.05$ ). Moreover, when the gait function measured by FAC was used as a GAS in subacute stroke patients, the better the function of Rivermead mobility index ( $β = 0.613$ , $p < 0.05$ ) and Korean-mini-mental state examination ( $β = 0.217$ , $p < 0.05$ ) than the other clinical factors, the higher the goal attainment raw score.

Conclusion: Functions, including cognitive function, should be included when setting goals to improve the gait function and should be considered when developing the neurological physiotherapy programmes. This study helps physicians and physical therapists who first apply functional gait assessment as a GAS to set the initial goals and improves patient and caregiver motivations by applying GAS to patients with lower initial cognitive levels.

Keywords:

Introduction

Stroke is the most common cause of acquired disability, and stroke-related disabilities can include symptoms such as muscle weakness, sensory abnormalities, and cognitive impairment.^1,2 Among them, gait disturbance is the most common one of stroke, and falls that occur during walking after discharge from the hospital are a major cause of concern.³ Therefore, the recovery of independent mobility due to the acquisition of safe and stable walking patterns is one of the main goals of stroke rehabilitation.⁴ Rehabilitation interventions should consider a patient’s medical, neurological, psychological, and social conditions.⁵ Correct goal setting for patients is one of the most important aspects of rehabilitation; however, it is difficult due to variables such as neurological impairment status and health status (age and stage after stroke onset).⁶

Setting goals before providing rehabilitation interventions and determining whether they are achieved by intervention is important for evaluating the rehabilitation outcomes,⁷ and in recent years, patient-centred approaches have been recommended as an important guiding principle for rehabilitation services.^8,9 Patient-centred approach means that medical specialists make care decisions with patients, and are guided by the patient’s needs, preferences, and knowledge of the patients.¹⁰ Furthermore, this approach might improve treatment outcomes, increase patient satisfaction, and reduce medical costs.¹¹ Goal attainment scaling (GAS) scores can be used to assess the achievement of specific rehabilitation goals which the patients or their caregivers participate to set up.¹² GAS is valuable for assessing the progress of neurological rehabilitation patients adequately.¹³

GAS is a method to create personalised rating scales to quantify the procedure towards defined goals.¹⁴ First described by Kiresuk and Sherman in the 1960s to evaluate programmes in mental health centres,^15,16 this method has been applied to a variety of complex interventions, including medical area.¹⁷ The GAS process involves three stages: identifying individualised goals, developing a rubric of measures to define the possible results for each goal, and later, assessing the goal achievement.¹⁸ The scoring method is based on a five-point scale: if a patient reaches the expected level of achievement, the scores of ‘0’, ‘1’, and ‘2’ mean that they did ‘slightly’ and ‘much’ better than expected, respectively, and the scores of ‘−1’ and ‘−2’ mean that they did ‘slightly’ and ‘much’ worse than expected.¹⁷ Therefore, GAS is used in many areas where accurate goal setting is fundamental to treatment planning and has advantages for enhancing the patient’s motivation and engagement.^7,14,19

Previous studies have reported that GAS is effective in patients with a variety of conditions, including chronic geriatric disease, cognitive rehabilitation, cerebral palsy, multiple sclerosis,^7,20 and for the rehabilitation of patients with chronic brain injury.²¹ However, there is a lack of research on the application of GAS in the subacute phase, which provides the greatest opportunity for recovery.²²

Many stroke patients have difficulty in gait, and thus, gait rehabilitation aimed at improving the gait ability is crucial for patients and their caregivers.²³ Functional ambulation category (FAC) is a clinical measure of gait ability that is simple to use, easy to interpret, and cost-effective,²⁴ and was also found to be sensitive to changes in patient conditions in a study of stroke patients who were unable to perform gait unassisted during the early stages of rehabilitation.²⁵

In this study, we divided subacute stroke patients into four groups according to whether the goals were achieved using raw GAS scores calculated based on the FAC score, and then conducted an analysis to identify factors that affected the gait functions of patients. Thus, we aimed to determine the differences in clinical assessment score according to the raw GAS score, the correlation between the raw GAS score and the clinical assessment score, and the factors affecting the raw GAS score.

Materials and Methods

Participants

This study was carried out to analyse the medical records of 129 subacute stroke patients who received physical therapy at the B Hospital in Seongnam-si, Gyeonggi-do, South Korea. In this study, the subacute phase was defined as 90 days after the stroke onset.²⁶

Inclusion criteria were patients with subacute stroke within 90 days of onset and patients who completed a discharge evaluation one month after the inpatient evaluation and rehabilitation services and underwent an FAC evaluation with GAS.

Exclusion criteria were patients with neurological conditions other than stroke, vision and hearing impairment, aphasia, poor general condition, and inability to cooperate for other reasons.

This study was approved by the Institutional Review Board of Bundang Cha Hospital, CHA University (2021-06-008). General characteristics of participants were collected from the evaluation form for patients with stroke and included gender, age, body mass index, stroke type, lesion site, and time from the stroke onset. No personal information, such as patient names, addresses, or contact information, was collected.

Procedure

We analysed the medical records of 129 subacute stroke patients who were treated with physical therapy from January 2018 to December 2020. FAC was used to set goals with patients and their caregivers after the initial assessment, and a raw GAS score was calculated to determine whether the goals had been achieved one month later. Participants were allocated to one of the four groups based on their raw GAS scores, i.e., to the GAS group −1, 0, 1, or 2. The general characteristics and clinical assessment scores of participants in these groups were analysed. None of the participants in this study had a score of −2, therefore, the −2 GAS group was excluded from statistical analysis.

Physical therapeutic intervention

In this study, 30min of physical therapy was provided to the participants in the morning and afternoon. The physical therapist who performed the treatment had more than 3 years of specialised knowledge. Exercise programmes, including functional electrical stimulation, bicycles training, and incline beds training, were performed according to the functional status and patient condition. In addition, rehabilitation training, including occupational therapy, speech therapy, and psychotherapy, was provided for more than 15h per week for one month.

Measurement

Manual muscle test

Manual muscle test (MMT) utilises gravity and resistance to effectively assess the strengths and functions of individual muscles and muscle groups and categorises the strength classes as follows²⁷: Normal meant a maximum strength of 100%, which meant the participant withstood maximum resistance against gravity. Good meant 80% of maximal strength, meaning moderate resistance against gravity. Fair meant 50% of maximum strength, meaning the participant was able to move through normal range of motions against gravity. Poor meant 20% of maximum strength and the ability to move up to the normal range of motions without gravity. Trace meant 5% of maximum strength, and no visible joint movement, but with muscle contraction on palpation. Zero meant 0% of maximum strength, meaning no muscle contraction.

The following areas were measured: shoulder flexors, shoulder abductors, elbow flexors, elbow extensors, wrist flexors, wrist extensors, finger flexors, finger extensors, finger abductors, finger adductors, hip flexors, hip extensors, hip abductors, hip adductors, knee flexors, knee extensors, dorsiflexors, and plantar flexors. Thus, 18 areas were scored on the left- and right-hand sides and then summed. The final scores ranged from a possible minimum of 0 to 3,600.

Berg balance scale

The Berg balance scale (BBS) was used to evaluate postural maintenance, postural control during voluntary exercise, and response to external perturbations. The BBS is used as a functional balance test in the elderly and patients with neurological diseases to assess the risk of falling, and is composed of 14 items rated using a five-point scale ranging from 0 to 4 (with a maximum score of 56 points). A score of 0 indicates an inability to perform the task, while a score of 4 indicates that a patient can perform the task independently. Scores of 0–20 indicate a balance disorder, of 21–40 show a moderate balance ability, and of 41–56 point a good balance ability. The BBS can be used to assess the static and dynamic aspects of balance.^28,29

Rivermead mobility index

The Rivermead mobility index (RMI) is the only mobility measurement endorsed by the U.S. Agency for Healthcare Policy and Research.³⁰ It was designed specifically to assess the stroke patients and is recognised as a useful measurement method for assessing the patient’s functional status before and after rehabilitation.³¹ This RMI involves score by a therapist during an interview with the patient and caregiver. It consists of 15 questions, and responses were scored as 1 if the patient could perform the function, and as 0 if the patient could not. Thus, the minimum score was 0 and the maximum was 15.

Motor assessment scale

The motor assessment scale (MAS) is a widely used measure of functional recovery after stroke. The scale involves the assessment of eight items related to motor function and one item related to muscle tone.³² MAS can be used to assess the abilities to lie down, get up, and sit on a bed, sitting balance, standing from a sitting position, upper extremity function, hand movements, advanced hand movements, and gait. MAS has been used as a measurement tool in upper extremity rehabilitation, standing training, lower extremity strength training, and rehabilitation to improve gait and balance in stroke patients.^33,34 Each of the eight items is rated using a scale of 0–6, and thus, the scores range from 0 to 48.³⁵

Trunk impairment scale

The trunk impairment scale (TIS) was developed to assess the motor impairment after stroke and consists of seven items that assess the static and dynamic postural balance and trunk coordination while seated. The scores range from 0 point to 23 points.³⁶

Functional ambulation category

The FAC scale is a commonly used clinical gait assessment scale that categorises the gait ability into six levels based on the amount of physical support required and has high reliability and sensitivity to change over time in patients with post-stroke hemiplegia.^37,38 FAC level 0 means that the patient is unable to performing gait unaided and requires the assistance of two or more people. FAC level 1 means that the patient requires constant assistance from one or more people to balance while gait without falling. FAC level 2 means the patient needs intermittent assistance to walk. FAC level 3 means that the patient can gait unassisted, but a caregiver must be present for safety reasons. FAC level 4 means that the patient can gait independently, but needs help with stairs or inclines. FAC level 5 means the patient can perform gait independently, including upstairs.

Korean-mini-mental state examination

The Korean-mini-mental state examination (K-MMSE) is most widely used as a useful screening test for cognitive impairment after stroke and as a clinical method for grading cognitive impairment. It consists of 30 items that provide information on orientation, attention, learning, calculation, delayed recall, and organisation.³⁹

Goal attainment scaling

GAS was applied to all patients with stroke, and the raw GAS scores were determined using a five-point Likert scale by evaluating the results (MMT, BBS, RMI, MAS, TIS, FAC, and K-MMSE).

The three most urgently needed improvements were selected for GAS based on the initial clinical assessment scores. Patients’ and caregivers’ opinions were obtained through interviews during goal setting. It can be difficult to set goals for cognitive impairment using the GAS.¹⁵ Therefore, in this study, goals were set with the help of a caregiver or family member when the patient had difficulty communicating with the therapist due to cognitive decline.

This was followed by a discussion with the rehabilitation team (rehabilitation physician, nurse, physical therapist, occupational therapist, psychologist, speech therapist, social worker, and dietitian). Based on the International Classification of Functioning, Disability and Health (ICF) categories of functioning, activity, and participation, goals were set according to the areas considered to most require improvement after considering the health status, degree of neurological impairment and brain damage, age, gender, and prognosis after hospital discharge. When setting the GAS goals, we applied the ‘SMART’ principle to ensure the goals were specific, measurable, achievable, realistic, and time bound.¹⁴ At the rehabilitation team meetings, after a patient had completed four weeks of rehabilitation intervention, the raw GAS scores were evaluated, and the appropriateness of goal setting was discussed. Raw GAS scores ranged from −2 points to 2 points, where −2 points meant ‘worse than the initial state’, −1 point meant ‘not reached the expected goal, but better than the initial state’, 0 point meant ‘accurately predicting and achieving the goal’, 1 point denoted ‘slightly exceeding the goal’, and 2 points indicated ‘greatly exceeding the goal’. Patients were allocated to one of the four groups based on the raw GAS scores, i.e., to the GAS groups −1, 0, 1, or 2 (no patient had a raw GAS score of −2).

Many studies have used standardised GAS scores to assess the raw scores comprehensively in terms of goal achievement. Standard GAS scores were calculated with a formula. The standard GAS score can be expressed as a t-score. However, in this study, the raw GAS scores calculated from the FAC to set goals with patients and their caregivers after the initial assessment, were used to determine the effect of other factors on achieving the goal of gait one month later.

Statistical analysis

FAC levels were used to calculate the raw GAS scores, and the one-way analysis of variance (ANOVA) and $χ^{2}$ test were performed to analyse the differences in the general characteristics and mean goal-setting values according to the raw GAS scores. Repeated measures ANOVA was used to analyse the differences between initial and final clinical assessment scores according to the GAS raw score, and then a one-way ANOVA was used to compare the differences between initial and final clinical assessment scores. Post-hoc tests were performed using Scheffe’s test, followed by Welch’s test and Dunnett’s T3 post-hoc test if equal variances were not assumed.

A paired t-test was performed to examine the significance of changes in clinical evaluation scores after intervention in the four GAS groups. Pearson’s correlation analysis was performed to examine the relationship between raw GAS score and variables that might affect the gait function. Multiple regression analysis was conducted to identify the general characteristics and clinical assessment scores of participants that affected the raw GAS scores after checking for multicollinearity among the independent variables and no autocorrelation in the dependent variable. The analysis was conducted using SPSS version 21.0 for Windows (IBM, USA), and statistical significance was accepted for the $α$ -values less than 0.05.

Results

General characteristics of participants based on raw GAS scores

There were no significant differences in general characteristics of the raw GAS score groups. In case of gender, males outnumbered females among the 129 participants (63.6% versus 36.4%). Moreover, males outnumbered females in the −1, 0, and 1 raw GAS score groups (Table 1); there were slightly more females in the two raw GAS score groups. The average participant age was 62.33 years, and the average age was non-significantly lower in the higher GAS score groups. Body mass index was similar in the four groups (average of 23.3). As regards stroke types, among all the participants, 53.5% had a cerebral haemorrhage and 46.5% had cerebral infarction. Groups −1, 1, and 2 had a higher percentage of cerebral haemorrhage, while group 0 had a higher percentage of cerebral infarction, but these differences were not significant. Regarding lesion sites, 31.8% were in the right hemisphere, 25.6% were in the left hemisphere, 21.7% were in both, and 20.9% were in cerebellum. The differences between the groups were that group −1 had higher percentages of bilateral hemispheres (33.3%) and cerebellum (41.7%) than the other groups, group 0 had a higher percentage of right hemisphere (38.4%), group 1 had higher percentages of bilateral hemispheres (32.3%) and right hemisphere (29%), and group 2 had a higher percentage of left hemisphere (53.8%), but these differences were not significantly different. Average time from stroke onset was 33 days and was similar in the four groups.

**Table 1. General characteristics of participants based on the raw GAS scores.**
	$- 1$	0	1	2	Total
Groups	$n = 12$ (9.3%)	$n = 73$ (56.6%)	$n = 31$ (24%)	$n = 13$ (10.1%)	$n = 129$ (100%)	F $/ χ^{2}$	p
Sex
Male	7	46	23	6	82	3.365	0.339
	(58.3)	(63)	(74.2)	(46.2)	(63.3)
Female	5	27	8	7	47
	(41.7)	(37)	(25.8)	(53.8)	(36.4)
Age (years)	$63.5 \pm 17.2$	$64.5 \pm 13.2$	$58.90 \pm 17.9$	$57.15 \pm 18.8$	$62.33 \pm 15.5$	1.517	0.213
Body mass index	$23.6 \pm 2.5$	$23.2 \pm 2.9$	$23.4 \pm 2.9$	$23 \pm 2.4$	$23.3 \pm 2.8$	0.098	0.961
Aetiology
Haemorrhage	7	34	19	9	69	3.569	0.312
	(58.3)	(46.6)	(61.3)	(69.2)	(53.5)
Infarction	5	39	12	4	60
	(41.7)	(53.4)	(38.7)	(30.8)	(46.5)
Lesion
Right hemisphere	2	28	9	2	41	14.312	0.091
	(16.7)	(38.4)	(29)	(15.4)	(31.8)
Left hemisphere	1	19	6	7	33
	(8.3)	(26)	(19.4)	(53.8)	(25.6)
Bilateral hemispheres	4	13	10	1	28
	(33.3)	(17.8)	(32.2)	(7.7)	(21.7)
Cerebellum	5	13	6	3	27
	(41.7)	(17.8)	(19.4)	(23.1)	(20.9)
Post-stroke duration	$29.9 \pm 18.9$	$33.3 \pm 19.7$	$32.1 \pm 18$	$36.46 \pm 17.4$	$33 \pm 18.8$	0.279	0.840
(days)

Notes: * $p < 0.05$ . Values are presented as $mean \pm standard$ deviation ( $M \pm SD$ ) or number (%).

Differences between raw GAS score changes after the intervention

A comparison of intervention-induced changes in raw GAS scores showed higher raw GAS scores were significantly associated with greater improvements for all items (Table 2).

**Table 2. Differences between the raw GAS score changes after intervention.**
Variable	Group −1 (n=12)	Group 0 (n=73)	Group 1 (n=31)	Group 2 (n=13)	F	p	T3/Scheffe’s post-hoc test
MMT	$204.17 \pm 123.40$	$272.19 \pm 205.58$	$408.45 \pm 241.61$	$590.38 \pm 265.91$	$10.801$	0*	a, $b < d$
							$b < c$
BBS	$8.08 \pm 8.44$	$15.21 \pm 11.79$	$20.39 \pm 13.64$	$31 \pm 13.14$	9.206	0*	a, $b < d$
							$a < c$
TIS	$4.25 \pm 3.49$	$4.40 \pm 3.36$	$6.58 \pm 4.37$	$7.77 \pm 5.70$	3.161	0.040*	—
RMI	$2 \pm 1.21$	$3.70 \pm 2.61$	$6.06 \pm 3.21$	$9.62 \pm 1.12$	100.588	0*	$a < b < c < d$
FAC	$0 \pm 0$	$1.05 \pm 0.28$	$2.19 \pm 0.48$	$3.08 \pm 0.28$	271.204	0*	$a < b < c < d$
MAS	$7.83 \pm 5.56$	$10.78 \pm 7.83$	$11.81 \pm 9.96$	$22 \pm 12.90$	4.328	0.012*	a, $b < d$
K-MMSE	$2.75 \pm 2.73$	$3.22 \pm 2.72$	$5.84 \pm 6.49$	$7.69 \pm 7.62$	3.022	0.046*	—

Notes: Values are in the form of $M \pm SD$ . MMT: Manual muscle test, BBS: Berg balance scale, TIS: trunk impairment scale, RMI: Rivermead mobility index, FAC: functional ambulation category, MAS: motor assessment scale, and K-MMSE: Korean-mini-mental state examination. a: Group −1, b: group 0, c: group 1, and d: group 2. * $p < 0.05$ ( $a < b$ means that ‘b’ has a greater mean value than ‘a’ and there is a statistically significant difference between the two groups).

For the MMT scores, group −1 showed the smallest change of 204.17 points, followed by group 0 (272.19 points), group 1 (408.45 points), and group 2 (590.38 points), and these changes were statistically significant ( $p < 0.05$ ). Post-hoc tests showed significant difference of groups −1 and 0 from group 2 and between the groups 0 and 1.

In the BBS, group −1 showed the smallest change with 8.08 points, followed by group 0 (15.21 points), group 1 (20.39 points), and group 2 (31 points), and these changes were statistically significant ( $p < 0.05$ ). Post-hoc tests showed that groups −1 and 0 differed significantly from group 2, and that group −1 differed significantly from group 1.

TIS scores also showed significant differences ( $p < 0.05$ ). Group −1 showed the smallest change at 4.25 points, followed by group 0 (4.40 points), group 1 (6.58 points), and group 2 (7.77 points). Post-hoc tests revealed no significant differences.

As regards the RMI scores, the −1 GAS group showed the smallest change of 2 points, followed by group 0 (3.70), group 1 (6.06), and group 2 (9.62), and these were significantly different ( $p < 0.05$ ). Post-hoc tests showed that changes in the four groups were significantly different.

FAC scores revealed no change in group −1, followed by group 0 (1.05 points), group 1 (2.19 points), and group 2 (3.08 points), and these were significantly different ( $p < 0.05$ ). Post-hoc tests showed that changes in the four groups were significantly different.

MAS scores showed that group −1 demonstrated the smallest change of 7.83 points, followed by group 0 (10.78 points), group 1 (11.81 points), and group 2 (22 points), and these changes were significantly different ( $p < 0.05$ ). Post-hoc tests showed that changes in groups −1 and 0 differed significantly from the changes in group 2.

Regarding the K-MMSE scores, group −1 showed the smallest change at 2.75 points, followed by group 0 (3.22 points), group 1 (5.84 points), and group 2 (7.69 points), and these changes were significantly different ( $p < 0.05$ ). Post-hoc tests showed that changes in the four groups were similar.

Analysis of average target values based on the FAC raw scores

The FAC raw score changes were similar in the four groups (values ranged from 1 to 1.19; see Table 3).

**Table 3. Average target values based on the FAC raw scores.**
( $N = 129$ )
FAC raw score groups	$- 1$ ( $n = 12$ )	0 ( $n = 73$ )	1 ( $n = 31$ )	2 ( $n = 13$ )	F	p
Average goal-setting value	$1 \pm 0$	$1.05 \pm 0.28$	$1.19 \pm 0.48$	$1 \pm 0$	1.998	0.118

Note: Values are in the form of $M \pm SD$ . FAC: Functional ambulation category. * $p < 0.05$ .

Correlations between raw GAS score changes and improvements in clinical assessment factor scores

Significant positive correlations were observed for all variables (Table 4). In general, correlation analysis indicates the strength of an association, and an absolute value of r in the range of 0–0.19 is considered a very weak correlation, 0.2–0.39 is weak, 0.40–0.59 is moderate, 0.6–0.79 is strong, and 0.8–1 is a very strong one.⁴⁰

**Table 4. Correlations between raw GAS score changes and improvements in the clinical assessment factor scores.**
	Clinical evaluation scores
	MMT change	BBS change	TIS change	RMI change	MAS change	K-MMSE change	Raw score
Raw score	0.443*	0.418*	0.290*	0.615*	0.326*	0.326*	1

Notes: MMT: Manual muscle test, BBS: Berg balance scale, TIS: trunk impairment scale, RMI: Rivermead mobility index, FAC: functional ambulation category, MAS: motor assessment scale, and K-MMSE: Korean-mini-mental state examination. * $p < 0.05$ .

Regarding correlation between the clinical assessment scores and raw GAS scores, RMI scores showed the largest positive correlation with a correlation coefficient of $r = 0.615$ ( $p < 0.05$ ), followed by MMT ( $r = 0.443$ , $p < 0.05$ ), BBS ( $r = 0.418$ , $p < 0.05$ ), MAS and K-MMSE ( $r = 0.326$ , $p < 0.05$ ), and TIS ( $r = 0.290$ , $p < 0.05$ ).

Effect of clinical assessment item improvement on raw GAS scores

Multiple regression analysis was conducted to examine the effect of clinical evaluation factor score improvement on the raw GAS score (Table 5). First, we checked for multicollinearity between independent variables and the autocorrelation index of the dependent variable and found that the tolerance limit between the independent variables was greater than 0.1, the variance inflation factor (VIF) was less than 10, and the Durbin–Watson criterion range converged to 2, which confirmed no multicollinearity between the independent variables and autocorrelation of the dependent variable.

**Table 5. Effect of clinical assessment item improvement on the raw GAS scores.**
Dependent variable	Independent variable	B	SE	$β$	t	p	$F (p)$	$R^{2}$ (Adj.- $R^{2}$ )
FAC raw score	(Statistical constant)	0.225	0.700		0.322	0.748	6.847	0.476
	Age	$- 0.004$	0.004	$- 0.084$	$- 1.122$	0.264	(0*)	(0.407)
	Body mass index	$- 0.021$	0.022	$- 0.075$	$- 0.963$	0.337
	Sex	$- 0.067$	0.132	$- 0.041$	$- 0.503$	0.616
	Type of stroke	0.016	0.126	0.010	0.125	0.901
	Right hemisphere	0.101	0.164	0.060	0.617	0.538
	Left hemisphere	0.251	0.175	0.140	1.433	0.155
	Bilateral hemispheres	0.021	0.185	0.011	0.114	0.910
	Cerebellum	$- 0.113$	0.337	$- 0.025$	$- 0.335$	0.738
	Post-stroke duration (days)	0.002	0.003	0.050	0.667	0.506
	MMT change	0	0	0.129	1.342	0.182
	BBS change	$- 0.005$	0.007	$- 0.083$	$- 0.684$	0.495
	TIS change	$- 0.009$	0.020	$- 0.048$	$- 0.476$	0.635
	RMI change	0.148	0.024	0.613	6.216	0*
	MAS change	$- 0.005$	0.009	$- 0.061$	$- 0.572$	0.569
	K-MMSE change	0.036	0.013	0.217	2.743	0.007*

Notes: MMT: Manual muscle test, BBS: Berg balance scale, TIS: Trunk impairment scale, RMI: Rivermead mobility index, FAC: functional ambulation category, MAS: motor assessment scale, and K-MMSE: Korean-mini-mental state examination. Dummy variable (baseline): Sex (female), Type of stroke (infarction), and Aetiology (cerebellum). * $p < 0.05$ .

As shown in Table 5, general characteristics did not affect the raw GAS scores. Raw RMI score has the largest positive effect with $β = 0.613$ ( $p < 0.05$ ), which means that an increase in RMI by one point increased the raw GAS score by 0.613 (61.3%). This was followed by K-MMSE improvement ( $β = 0.217$ , $p < 0.05$ ), meaning that a one-point increase in K-MMSE was associated with a 0.217 (21.7%) increase in the raw GAS score. This factor had a relatively high explanatory power of 40.7% (adj.- $R^{2} = 0.407$ ) for total variance. The regression model used was also appropriate with $F = 6.847$ ( $p < 0.05$ ).

Discussion

This study investigated the effects of physical therapy using GAS on the gait function in subacute stroke patients and the factors that require consideration when setting the goals using GAS. Results of this study suggested that higher raw scores on the GAS were associated with higher clinical assessment scores and there was a correlation between raw scores on the GAS and improvement scores on clinical assessment items. Moreover, when the gait function measured by FAC was used as a GAS in subacute stroke patients, the better the function of RMI and K-MMSE than the other clinical factors, the higher the goal attainment raw score.

Initial clinical assessment scores did not differ significantly from the raw GAS score. However, the −1 group did not achieve their goals and tended to have lower initial cognitive status and lower functional levels than the 0, 1, and 2 groups who met or exceeded their goals. Later clinical assessment scores were higher for all items with higher raw GAS scores. The −1 group had the lowest initial cognitive status and the lowest cognitive improvement, suggesting that stroke patients with low cognitive status are less likely to respond to physical therapists’ instructions and engage in rehabilitation.⁴¹

Regarding post-intervention changes in clinical assessment scores in the four groups, all assessment factors showed statistically significant differences, and higher raw GAS scores resulted in greater changes in the clinical assessment scores. Furthermore, clinical assessment scores of the four groups were all significantly higher after intervention, and improvements were significantly greater in the higher raw GAS score groups.

It has been reported that functional level immediately after stroke is associated with better cognitive recovery in subacute stroke patients, and that a higher initial functional level is associated with better cognitive recovery.⁴² In this study, for within-group changes between baseline and post-test, the K-MMSE scores were 2.75 on average in the −1 group, 3.22 in the 0 group, 5.84 in the 1 group, and 7.69 in the 2 group, indicating that a higher raw GAS score was associated with a greater improvement in the K-MMSE scores and functional levels. Studies of cognitive recovery in subacute stroke patients reported an average increase in K-MMSE score of 3.14 points between one month and three months after the stroke onset.⁴² Furthermore, we found that higher cognitive status of a patient was associated with better functional performance, which concurs with previous studies which showed that the level of cognitive function at admission is associated with the goal achievement for inpatient neurorehabilitation.⁴³

When the difference in the mean goal-setting values was confirmed according to the raw score of FAC, we found that mean values were similar in the four groups (ranging from 1 point to 1.19 points). Setting goals is the most difficult aspect when using the GAS, as goals must be realistically achievable after rehabilitation.⁴⁴ Therefore, in this study, we set a goal of improving FAC by one level on average. Preivious studies reported that the ratio of patients with a raw GAS score of −2 to those with a raw GAS score of 2 varies from 0.5–1 to 10, which were consistent with that found in this study (10.1% of the original score 2 group).⁴⁵

The correlation between improvement scores for the clinical assessment factors and raw GAS scores was significantly positive for all variables; that is, the higher raw GAS scores were correlated with higher RMI, MMT, BBS, MAS, K-MMSE, and TIS improvement scores. The correlations showed that general characteristics did not affect the raw GAS scores. Notably, the results of the regression analysis showed that higher RMI and K-MMSE scores are more associated with higher raw GAS scores than the other clinical factors.

Oh et al. also reported an association between cognitive function recovery and functional status in subacute stroke patients.⁴² Taken together, these results suggest that functional recovery, including cognitive function, must be supported to improve gait beyond the expected goals. Therefore, initial cognitive level should be considered when setting the goals for subacute stroke patients, and rehabilitation programmes should include cognitive improvement goals to improve gait.

Patients show better treatment outcomes when they are involved in setting their goals.⁴⁶ However, there are challenges associated with applying the goal-setting scales to patients with cognitive impairment.¹⁵ Therefore, in most studies, goal-setting scales have been applied to stroke patients who are able to obtain a score ≥ 23 on MMSE.

In a previous study on the impact of patient-centred goal setting on the rehabilitation service outcomes in subacute stroke patients, rehabilitation service outcomes of an experimental group in which therapists and patients jointly set goals using a goal attainment scale were compared with the outcomes in a control group. When compared with standard GAS scores, clinical assessment scores related to mobility were found to be significantly better in the experimental group than in the control group.⁴⁷ In a study performed by Jung et al. on the utility of the GAS for subacute stroke patients undergoing intensive rehabilitation, it was reported that the GAS improved goal-setting ability and had a positive impact on rehabilitation.⁴⁸

Because the process of setting goals can often be motivated, and because it is important to involve caregivers, who spend most time with the patients, we applied the GAS to patients with cognitive decline and reduced response to physical therapist instructions delivered during interviews with caregivers. Therefore, in this study, we expected that goal setting by caregivers may have a positive effect on patient’s gait function.

This study has several limitations. First, it lacks adequate information on the number of treatments and confounding variables that potentially influence rehabilitation. Second, this study was retrospective, using data from a single institution, and included a small number of patients over a three-year study period. Therefore, there is a difference in the proportions of participants in the GAS groups; however, it is consistent with the results of previous studies that the lowest proportion of people are in the –2 and 2 groups and the highest proportion are in the 0 group. Although this is a natural phenomenon, the generalisation of our findings is limited.

Unlike most previous studies that analysed patients with an initial cognitive level of ≥20, we applied GAS to patients with cognitive decline who were less responsive to the instructions of physical therapists through interviews with their caregivers. In addition, we analysed the raw score data of patients for whom the FAC assessment was used as a GAS to identify factors that should be considered when setting the goals. This study helps physicians and physical therapists who first apply functional gait assessment as a GAS to set the initial goals and improves patient and caregiver motivations by applying GAS to patients with lower initial cognitive levels. Further research at various medical centres would help to overcome these limitations.

Conflict of Interest

The authors report no declarations of interest.

Funding/Support

There were no sources of funding or grant support for this study.

Author Contributions

The concept/idea/research design for this work was that of Jung-Min Hong. Both Jung-Min Hong and Min-Hee Kim were involved in writing this paper. Data collection and analysis had been made by Jung-Min Hong, while the project management and consultation (including review of manuscript before submitting) were made by Min-Hee Kim.

Ethics Approval

The IRB Approval No. is 2021-06-008.

ORCID

Jung-Min Hong https://orcid.org/0009-0009-5995-9009

Min-Hee Kim https://orcid.org/0000-0001-6072-9459

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Vol. 45, No. 01

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Received 13 October 2023

Accepted 2 February 2024

Published: 21 March 2024

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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) License which permits use, distribution and reproduction, provided that the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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