Skip to main content

Predictors of ambulatory recovery and walking proficiency in community-dwelling stroke survivors: a cross-sectional study

Abstract

Background

Although the major goal of rehabilitation is to return a stroke survivor (SSv) to as close to their pre-stroke functioning, limitation in ambulatory recovery and walking proficiency is the major impediment. Despite the importance of walking to the outcomes in stroke, factors predicting its recovery remain unclear. This study therefore was aimed at exploring the predictors of ambulatory recovery and walking proficiency in community-dwelling SSv.

Methods

This study involved 164 (83females) SSv from four tertiary health institutions in Nigeria. Ambulatory level and status was assessed using Functional Ambulatory Classification, motor function using the Fugl-Myer Assessment scale (lower limb), and ambulatory/waking endurance using the 6-min walk test. Ambulatory capability was assessed using the Lower Extremity Functional Scale, ambulatory self-confidence using the Ambulatory Self-Confidence Questionnaire, and functional ambulatory profile using the Modified Emory Functional Ambulation Profile. Mobility was assessed using the Modified Rivermead Mobility Index, functional mobility using Time Up and Go, balance using the Berg Balance Scale, and cognitive function using the modified Mini-Mental State Examination. Spatial indexes were assessed using the Footprint method and temporal variables using a stopwatch and gait speed on a 10-m walkway. Data was analyzed using multiple regression analysis at p ≤ 0.05.

Results

Participants (mean age = 54.3±11.36 years) have had stroke for 12.9 ± 17.39 months and spent 9.82 ± 13.19 months in hospital admissions. More (65.2%) had ischemic stroke with 54.3% of them having left hemispheric stroke. The predictors of ambulatory onset in SSv were stroke duration and length of stay in hospital admission contributing 40.3% (β = 0.403) and 17.6% (β = 0.176) respectively to the variance. Mobility (β = 0.249, p < 0.001), gait speed (β = 0.185, p = 0.012), paretic double-limb support time (β = 0.155, p = 0.03), balance (β = 0.334, p < 0.001), and cognition (β = 0.155, p = 0.01) were predictors of ambulatory self-confidence contributing 59.5% to the variance. Balance (β = 0.363, p < 0.001) and mobility (β = 0.155, p = 0.015) were predictors of ambulatory capability contributing 52.9% to the variance. Balance (β = −0.489, p < 0.001), paretic double-limb support time (β = 0.223, p = 0.003), gait speed (β = −0.181, p = 0.022), and paretic swing phase duration (β = 0.177, p = 0.01) were predictors of functional ambulatory profile (p < 0.05) contributing 52.9% to the variance. Gait speed (β = −0.648, p < 0.001) and step length (β = −0.157, p = 0.003) were predictors of walking endurance contributing 76.5% to the variance.

Conclusion

Ambulatory recovery and walking proficiency depend on the interplay among duration of stroke and length of hospitalization on the one hand and balance performance, cognitive function, and the spatiotemporal integrity of the affected limb on the other hand.

Introduction

The major goal of rehabilitation in stroke is to return a stroke survivor to an independent functioning as close to their pre-stroke status as possible. One of the major aspirations of a stroke survivor and their family alike is when and how to return to walking. However, a clinician is not only looking out for walking but the recovery of effective and efficient walking to reduce dependency in activity of daily living and burden of care on the informal caregivers. Hence, the focus is on community re-integration and return to productive lifestyle. Therefore, the recovery of function is not qualified but quantified for effective outcomes.

Stroke is a major cause of disability of adult onset and one of the major causes of loss of productivity in an individual who has been hitherto active before the onset of stroke. Therefore, recovery of ambulatory performance and walking proficiency is both the concern of everyone for improve quality of life [1,2,3]. The motor impairments in stroke survivors do not only result in deficiency ambulation, it negatively affects their ability to perform everyday activities without assistance resulting in activity limitation and participation restriction [4]. The impairment and dysfunction in walking typified by obvious gait asymmetry is a usual difficulty which occurs in the majority of stroke survivors as they recover from stroke especially in those with ischemic stroke and of older age [5,6,7]. This walking disability becomes the common obstacle affecting their community participation, community functioning, and productive lifestyle [1, 8]. It hinders their reaction time to challenges within their environment especially in negotiating obstacles, climbing stairs, walking on inclines, and uneven surfaces [9]. Hence, evaluation of motor function recovery typified by prior movement reemergence like in pre-stroke stage is key in stroke patients’ assessment and a fundamental part of stroke rehabilitation to project for adequate societal re-integration and community participation especially from the onset of a stroke [10, 11].

The effective return of stroke survivor to the community depends on the proficiency of the independent walking and performance. Hence, walking rehabilitation and gait recovery is a major focus for stroke survivors and substantial efforts and resources are being deployed to aid the walking recovery post-stroke [12,13,14,15]. More importantly that the ability to walk independently after stroke event is a crucial functional goal that is used for gauging recovery and ability to participate in activities of daily living [16,17,18]. Return to walking also has the potential of preventing or reducing certain secondary musculoskeletal and cardiovascular complications post-stroke [13, 19]. Hence, it is pertinent to understand the determinants of effective walking performance by exploring the predictors of ambulatory efficiency in stroke survivors.

Despite the importance of walking to the outcomes in stroke, factors predicting its recovery remain unclear. Although previous studies have explored the predictors of motor and functional recovery after stroke [7, 20], the predictors of ambulatory recovery and walking proficiency in stroke survivors have not been well delineated. Neurological and functional recovery has been reported to improve rapidly within the initial 6 months post-stroke which continues even beyond, though not as rapidly [7, 21,22,23]. Nevertheless, normal walking function ability in stroke survivors is affected by several factors such as cognitive dysfunction, cognitive task, motor task, and cognitive-motor interference [24, 25]. This is because many factors affect effective walking as there are many indexes that contribute to functional ambulation.

Many stroke patients are discharged home with cares on the informal caregivers. Hence, the recovery of ambulatory function and proficiency in walking will help in reducing the burden of care at home and improve community functioning thereby reducing the possibility of other secondary complications. Although many studies have focused on improving walking functioning in stroke survivors, the factors predicting the ambulatory indices especially the specificity of such factors to specific ambulatory function remain elusive. More importantly, the contribution of spatiotemporal indexes with balance and cognitive factors to productive ambulation remain a subject for debate. Thus, it will be vital to determine the precise predictors of such indices which will aid in tailoring gait rehabilitation strategies. This study, therefore, explores the predictors of ambulatory recovery and walking efficiency in community-dwelling stroke survivors to contribute to clinical decision-making in effective stroke rehabilitation. Hence, the question is, what are the predictors of ambulatory recovery and walking proficiency in community-dwelling stroke survivors?

Methods

The study was a cross-sectional analytical which involved 164 (83 females) stroke survivors from four tertiary health institutions in Kano State, Nigeria. Ethical approval for the study was obtained from the Health Research Ethics of Committees of Kano State Ministry of Health, Aminu Kano Teaching Hospital, and College of Medicine, University of Lagos. This study involved ambulatory (with or without support) stroke survivors who had been discharged to the community and are still attending rehabilitation services in the chosen hospitals. They were included if they could walk 10 m with or without walking aid or human assistance but with a Mini-Mental State Examination (MMSE) score of at least 24. Individual with psychiatric disorders, seizure disorders, severe cardiopulmonary compromise, and severe visual and auditory defects and those with history of neurological and musculoskeletal conditions that would affect walking efficiency were excluded from the study. The sample size was calculated using a standard formula, n= Z2 SD2/d2 [26] where n is the required sample, Z is the standard normal variate at 5% type 1 error which is constant = 1.96, SD is the standard deviation derived from previous similar study = 0.30, and d is the absolute error or precision or acceptable margin of error at 0.05. With 15% possible attrition added to the calculation, a sample of 159 participants was obtained, but this study involved 164 participants.

Sociodemographic and clinical characteristics such as stroke onset, duration, laterality, type, and comorbidity were assessed using proforma. Their ambulatory status pre-stroke, ambulation onset post-stroke, and duration of ambulation post-stroke were also assessed. Ambulatory indices, ambulatory status, motor function, ambulatory endurance, ambulatory capabilities, ambulatory self-confidence, functional ambulatory profile, mobility level, and functional mobility, were assessed. Spatial variables, stride length, step length, and stride/step width, as well as temporal variables, speed, cadence, gait cycle duration, paretic limb stance phase duration, paretic limb swing phase duration, and paretic initial double-limb support time, were also assessed.

The assessment of ambulatory characteristics entails assessing the ambulatory status, motor function, ambulatory/walking endurance, ambulatory capabilities, ambulatory self-confidence, functional ambulatory profile, mobility level, and functional mobility. Ambulatory status was assessed using Functional Ambulatory Classification (FAC): to classify ambulatory status/level. A reliable, valid, and responsive FAC scale which is a 6-point scale was used to assess the amount of support the patient requires when walking [27]. Motor function was assessed using the Fugl-Myer Assessment scale (lower-limb part) developed by Fugl-Myer [28] in 1975. The motor scale is recommended as an outcome measure for better motor recovery assessment in stroke patients [29]. Ambulatory/waking endurance was assessed using a 6-min walk test [30]. Ambulatory capability was assessed using the Lower Extremity Functional Scale. The scale is valid and reliable and is sensitive to changes in lower limb dysfunction among stroke survivors [31]. Their ambulatory self-confidence was assessed using the Ambulatory Self-Confidence Questionnaire (ASCQ) [32], while their functional ambulatory profile was assessed using he Modified Emory Functional Ambulation Profile which was reported to be a responsive, reliable, and valid scale [33]. The mobility level was assessed using Modified Rivermead Mobility Index which has its psychometric properties tested [34].

Their functional mobility was assessed using the Time Up and Go test which was shown to be valid and reliable [35]. Balance performance (dynamic and static balance) was assessed using Berg Balance Scale [36]. Their cognitive functional performance was assessed using the modified Mini-Mental State Examination [37].

In assessing their spatial indexes, their stride length, step length, and stride/step width were assessed using the Footprint method [38, 39]. Foot and marks were used to measure the respective distances between one point and another. Stride length was measured as distance between the heel strike of one foot to the next successive heel strike of the same foot using midpoint of the heel bisection as the reference point. Step length is the distance between the heel strike of one foot to the next successive heel strike of the other foot using midpoint of the heel bisection as the reference point. Their stride/step width or base of support was measured as the distance between midpoints of two opposite heels. To minimize/avoid measurement error and to ensure accuracy in the values/readings obtained, each parameter was measured at least twice, and the average value was taken and recorded. All measurements were done for both paretic and non-paretic extremities.

In assessing their temporal variables, a standard digital stopwatch was used [40]. The procedure involved measurement of time taken to carry out different activities in the gait cycle. In order to minimize/avoid measurement error and ensure accuracy in the values/readings obtained while measuring gait cycle and its components, each parameter was measured midway along the 10M track at least twice and the average value was taken and recorded. Except for speed and cadence, all measurements were done for both paretic and non-paretic extremities. The parameters assessed include gait speed, cadence, stride time/gait cycle duration, stance phase duration, swing phase duration, and initial double-limb support time.

Gait speed in meters per second was assessed using 10-m walk test which is recommended for assessing gait speed [41]. Their cadence was measured as number of steps per minute. Stride time which is the gait cycle duration was measured as time in seconds between the first heel contact of one foot (right or left) to the subsequent successive heel contact of the same foot. Stance phase duration was measured as the time in seconds when the reference lower limb is in contact with the ground. Swing phase duration was measured as time in seconds when the reference lower limb is not in contact with the ground. Initial double-limb support time was measured as the initial time in seconds when both feet are in contact with the ground.

Data analysis

Descriptive statistics of mean, standard deviation, frequency, and percentage were used to present the sociodemographic and clinical characteristics of the participants. Hierarchical and standard multiple regression analysis was used to determine the predictors of ambulatory recovery and walking proficiency. Statistical Package for Social Sciences (SPSS) version 25 was used for the analysis. The level of significance was set at p ≤ 0.05.

Results

The mean age of the participants was 54.3 ± 11.36 years. More of the participants were male (50.6%), had ischemic stroke (65.2%), and had left-sided affectation (54.3%) (Table 1). They have had stroke for 12.9±17.39 months and were hospitalized for 9.82±13.19 months. Their mean ambulatory onset after stroke was 1.86±2.89 months with post-stroke ambulatory duration of 11.14±16.30 months.

Table 1 Sociodemographic and clinical characteristics of the participants

Hierarchical multiple regression analysis for prediction of ambulatory onset post-stroke showed that only stroke duration and length of hospital stay predict ambulatory onset with each contributing 40.3% (β = 0.403) and 17.6% (β = 0.176) respectively to the regression model (Table 2). The values obtained in hierarchical multiple regression and presented in Table 2 were as a result of interaction between the independent (predictor) variables and the dependent (outcome) variables in the regression model using SPSS software. Standard multiple regression revealed mobility level (β = 0.249, p < 0.00), gait speed (β = 0.185, p = 0.012), paretic initial double-limb support time (β = 0.155, p = 0.025), balance (β = 0.334, p < 0.00), and cognition (β = 0.155, p = 0.011) to be the significant predictors of ambulatory self-confidence (Table 3). A total of 59.5% (R2 = 0.595) of the variance in the ambulatory self-confidence was predicted/explained by the final regression model (R2 = 0.595, F, df=10, =22.305, p ≤ 0.001). Apart from the said variables, all other variables were not significant (p > 0.005) predictors of ambulatory onset in stroke survivors (Table 3). Balance (β = 0.363, p < 0.001) and mobility level (β = 0.155, p = 0.015) are the significant predictors of ambulatory capability (p < 0.05); a total of 52.9% (R2 = 0.529) of the variance in ambulatory capability was explained by the final regression model (R2 = 0.529, F, df=10, = 17.039, p ≤ 0.001) (Table 4). Except for balance and mobility level, all other variables were non-significant (p > 0.005) predictors of ambulatory capability (Table 4). The values obtained in standard multiple regression and presented in Tables 3 and 4 were as a result of interaction between the independent (predictor) variables and the dependent (outcome) variables in the regression model using SPSS software. In all the regression analysis tables, the R2 represents the variance in the dependent variable while β is the standardized coefficient representing the individual contribution of the independent (predictor) variables to the variance of the dependent (outcome) variable.

Table 2 Hierarchical multiple regression analysis for the prediction of ambulatory onset and contributions of the respective predictors to the prediction model
Table 3 Standard multiple regression analysis for the prediction of ambulatory self-confidence
Table 4 Standard multiple regression analysis for the prediction of ambulatory capability

Furthermore, balance (β = −0.489, p < 0.001), paretic initial double-limb support time (β = 0.223, p = 0.003), gait speed (β = −0.181, p = 0.022), and paretic swing phase duration (β = −0.177, p = 0.011) are the significant predictors of functional ambulatory profile (p < 0.05); a total of 52.9% (R2 = 0.529) of the variance in functional ambulatory profile was explained by the final regression model (R2 = 0.529, F4, df=10, = 17.098, p ≤ 0.001) (Table 5). All other variables except the one mentioned above were not significant (p > 0.005) predictors of functional ambulatory profile in stroke survivors (Table 5). In addition, gait speed (β = −0.648, p < 0.001) and step length (β = −0.157, p = 0.003) are the significant predictors of walking endurance (p < 0.05); a total of 76.5% (R2 = 0.765) of the variance in walking endurance was explained by the final regression model (R2 = 0.765, F4, df=10, = 49.395.82, p ≤ 0.001) (Table 6). Apart from gait speed and step length, all other variables were not significant (p > 0.005) predictors of walking endurance. The values obtained in standard multiple regression and presented in Tables 5 and 6 were as a result of interaction between the independent (predictor) variables and the dependent (outcome) variables in the regression model using SPSS software. In all the regression analysis tables, the R2 represents the variance in the dependent variable while β is the standardized coefficient representing the individual contribution of the independent (predictor) variables to the variance of the dependent (outcome) variable.

Table 5 Standard multiple regression analysis model for the prediction of functional ambulatory profile
Table 6 Standard multiple regression analysis model for the prediction of ambulatory endurance

Discussion

This study explored the predictors of ambulatory recovery and walking proficiency among community-dwelling stroke survivors. The preponderance of ischemic stroke and the age of the participants in this study followed the global norms of stroke types of distribution and the average age of occurrence of stroke. It substantiates the fact that stroke is still common among productive age group leaving more previously productive able adult individuals unproductive or becoming totally dependent in the performance of activities of daily living. Hence, effort at reducing the incidence of stroke should be a concern of all with healthcare professionals being at the forefront of the vanguard.

The fact that ambulatory recovery and walking proficiency in stroke survivors are predicted by those factors that usually do not attract the attention of clinicians during plan for hospital discharge calls for paradigm in the clinical reasoning and clinical decision-making among experts in stroke care. There should be patient-centered team discussion and planning involving all experts in the stroke team and the informal caregivers for proper informed decision towards the discharge of the patient for effective community re-integration. This will enhance total re-integration of stroke survivors for return to productive lifestyle after hospital discharge. It will reduce the number of stroke patients that are discharged to the community and remain non-functional and disabled. This is important because the crucial constituent of daily physical activity depends on effective ambulatory functioning [42]. The outcomes of this study are crucial as they point to salient attributes especially spatiotemporal characteristics, balance, mobility, and cognition which if not well tailored towards the ambulatory retraining of a stroke survivor will be an impediment to the full and proficient walking performance. When these attributes are well marshalled, it will improve the functional mobility, ambulatory capabilities, ambulatory endurance, ambulatory activity, and ambulatory self-confidence of a community-dwelling stroke survivors, thereby improving their societal functioning. These results will shape and sharpen clinical practice for stroke rehabilitation. It will help the stroke care teams to make optimal plan and appropriate management decision for walking recovery in stroke survivors [20].

The fact that stroke duration and duration of in-patient hospital stay predict ambulatory onset post-stroke with stroke duration having the higher contribution calls for early ambulation and walking and gait training in stroke patients. Stroke patients who are stable clinically should be discharged early for comprehensive rehabilitation. This will go a long way in reducing disability sequel to stroke. Conversely, it will also reduce the burden of care on the informal caregivers. This corroborates that of a previous study that initiation of early rehabilitation strategies within first 2 weeks after a stroke enhances better outcome for independent functional recovery in stroke survivors [43]. This also corroborates that of earlier authors who had earlier concluded that using bedside measures at 1-week post-stroke time to walking independently post-stroke algorithm can accurately predict whether and when an individual patient can walk independently [44].

The outcomes of this study showed that one of the major approaches to better ambulatory self-confidence is to improve mobility level, gait speed, paretic initial double-limb support time, balance, and cognition. Hence, effective ambulatory retraining in stroke survivors should incorporate these factors together with improved balance performance and cognitive retraining. Hence, improving balance should be a major target of rehabilitation while improving the muscle strength. The significant prediction of endurance in the participants in this study corroborates that obtained in a previous study that improvement in motor function recovery is a predictor of improvement in ambulatory endurance [45]. This can be further explained by the report in a previous study that gait endurance is a predictor of ambulatory outcome in the first 6 months of hospital discharge post-stroke [46].

One of the major clinical applications of the outcomes of this study is the fact that it may equip clinicians in the effective walking performance retraining in stroke survivors. This is because the indexes such as walking endurance and stride length are major indicators for progress of gait performance [27]. This finding agrees with that of previous authors who reported that walking endurance impacts vitally in home and community walking activity after stroke [47]. The findings about the walking recovery are vital for the stroke survivors’ rehabilitation because detailed knowledge of recovery is crucial for rehabilitation and discharge planning [48]. In addition, detailed knowledge of walking performance increases chances of the ability to recover walking in stroke survivors [49].

The fact that gait speed predicts most of the ambulatory recovery characteristics can be explained by the fact that an improved self-confidence in ambulation with adequate balance will improve gait speed in an individual. A similar finding has been previously reported with the conclusion that practical ambulation ability is associated with gait speed and is valuably predicted by the gait speed [50]. In addition, fast walking speed is a useful early predictor to attain independent post-stroke community walking [51]. The outcome of this study suggests that attainment of good gait speed is an important determinant of successful ambulation. This is like those of previous reports that gait speed is a strong determinant of community mobility and that better walking speed is associated with community mobility in stroke [52, 53]. Previous studies have also concluded that gait speed is a suitable predictor of community walking post-stroke and that gait speed has exceptional ability to indicate community ambulation ability in stroke survivors [54,55,56].

The results of this study show the importance of balance in effective walking, as it significantly determines self-confidence, capability, and functional ambulatory profile during walking. This is important as more efficient walking pattern is obtainable in stroke survivors with higher balance performance [57]. This is clinically important as balance has been shown to have a vital role on home and community walking following stroke with its recovery ensuring safety and stability in ambulatory performance [47, 58]. Self-perceived balance is a helpful early predictor to achieve independent community walking post-stroke [51]. Hence, to attain improvement in stroke survivors’ ability to walk, functional balance should be a vital target during rehabilitation [58]. This is important as deficits in balance result in reduced ambulatory activity in chronic stroke survivors [59].

The fact that specific gait cycle components especially paretic initial double-limb support time and swing time were determinants of the performance of effective ambulation shows that return to walking in stroke survivors depends on the effective recovery of the affected limb especially in improving their ambulatory self-confidence and profile. Plummer-D’Amato et al. [60] had earlier reported that double-limb support involving paretic weight acceptance may impact on cognitive-motor interference in gait which could affect community ambulation in stroke. Paretic swing time was found to be a significant predictor of ambulatory profile. This supports the opinion that long-distance walking is importantly determined in post-stroke individuals by propulsion generation ability while walking [61]. It has been reported that quantifiable markers of post-stroke impaired walking performance include increased step length and stride time variability and decreased width variability as well as inter between limb difference in swing and pre-swing time variability [62]. The contribution of cognitive functioning to the self-confidence and effective mobility performance in the participants shows the importance of adequate mental orientation, awareness, and alertness to effective walking functioning. This is a pointer to the fact that cognition and walking confidence are related. Hence, in mild to moderate stroke survivors, global cognitive performance outcomes and gait performance change are strongly associated [63].

In summary, recovery of efficient ambulation in stroke survivors is determined by factors such as duration of stroke and in-patient hospital stay, gait speed, balance, mobility level, paretic initial double-limb support time, paretic limb swing time, paretic limb step length, and cognition. It is therefore expedient to focus on such factors in gait rehabilitation of stroke survivors especially while aiming at improving ambulatory recovery and walking proficiency in the stroke survivors. Also, identifying such factors will assist clinicians especially physiotherapists to know what to focus on to ensure sustainable recovery of walking proficiency for effective community and societal functioning and return to productive lifestyle after a stroke. The outcome of this study will enhance individualized rehabilitation for stroke patients for community functioning. It will enhance individualized therapy, and it has also provided more evidence for rehabilitation of stroke survivors for effective community re-integration and return to work. Because this study is a cross-sectional study, the generalization of the results should be cautiously applied. Hence, a clinical trial is needed to provide further evidence to this finding.

Conclusions

It can be concluded that, although ambulatory recovery and walking proficiency in stroke survivors are influenced by interplay among many factors, ambulatory recovery and walking proficiency in stroke survivors depend on the interplay among duration of stroke and length of hospitalization on the one hand and balance performance, cognitive function, and the spatiotemporal integrity of the affected limb on the other hand.

Availability of data and materials

Data will be made available by the corresponding author on reasonable request.

Abbreviations

ADL:

Activities of daily living

MMSE:

Mini-Mental State Examination

FAC:

Functional Ambulatory Classification

ASCQ:

Ambulatory Self-Confidence Questionnaire

SPSS:

Statistical Package for Social Science

References

  1. Ada L, Dean CM, Lindley R, Lloyd G. Improving community ambulation after stroke: the AMBULATE Trial. BMC neurology. 2009;9(1):8. https://doi.org/10.1186/1471-2377-9-8.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gbiri CA, Akinpelu AO. Relationship between post-stroke functional recovery and quality of life among Nigerian stroke survivors. Niger Postgrad Med J. 2013;20(1):29–33.

    PubMed  Google Scholar 

  3. Selves C, Stoquart G, Lejeune T. Gait rehabilitation after stroke: review of the evidence of predictors, clinical outcomes and timing for interventions. Acta Neurol Belg. 2020;120:783–90. https://doi.org/10.1007/s13760-020-01320-7.

    Article  PubMed  Google Scholar 

  4. Cawood J, Visagie S, Mji G. Impact of post-stroke impairments on activities and participation as experienced by stroke survivors in a Western Cape setting. South African J Occup Ther. 2016;46(2):10–5.

    Article  Google Scholar 

  5. Li M, Tian S, Sun L, Chen X. Gait analysis for post-stroke hemiparetic patient by multi-features fusion method. Sensors. 2019;19(7):1737. https://doi.org/10.3390/s19071737.

    Article  PubMed Central  Google Scholar 

  6. Wang Y, Mukaino M, Ohtsuka K, Otaka Y, Tanikawa H, Matsuda F, et al. Gait characteristics of post-stroke hemiparetic patients with different walking speeds. Int J Rehabil Res. 2020;43(1):69. https://doi.org/10.1097/mrr.0000000000000391.

    Article  PubMed  Google Scholar 

  7. Gbiri CA, Akinpleu AO, Ogunniyi A, Akinwuntan AE, Van Staden CW. Clinical predictors of functional recovery at six month post-stroke. Asian J Med Sci. 2014;6(1):49–54.

    Article  Google Scholar 

  8. Burgess JK, Weibel GC, Brown DA. Overground walking speed changes when subjected to body weight support conditions for nonimpaired and post stroke individuals. J Neuroeng Rehabil. 2010;7(1):1–1. https://doi.org/10.1186/1743-0003-7-6.

    Article  Google Scholar 

  9. Jaffe DL, Brown DA, Pierson-Carey CD, Buckley EL, Lew HL. Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev. 2004;41(3A):283–92. https://doi.org/10.1682/jrrd.2004.03.0283.

    Article  PubMed  Google Scholar 

  10. Levin MF, Kleim JA, Wolf SL. What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabil Neural Repair. 2009;23(4):313–9. https://doi.org/10.1177/1545968308328727.

    Article  CAS  PubMed  Google Scholar 

  11. Olawale OA, Julius SO. Evaluation of motor recovery in adult patients with hemiplegic stroke. Nigerian Quarterly J Hospital Med. 2006;16(1):10–3.

    Google Scholar 

  12. Olawale OA, Jaja SI, Anigbogu CN, Appiah-Kubi KO, Jones-Okai D. Exercise training improves walking function in an African group of stroke survivors: a randomized controlled trial. Clin Rehabil. 2011;25(5):442–50. https://doi.org/10.1177/0269215510389199.

    Article  CAS  PubMed  Google Scholar 

  13. Eng JJ, Tang PF. Gait training strategies to optimize walking ability in people with stroke: a synthesis of the evidence. Expert Rev Neurother. 2007;7(10):1417–36. https://doi.org/10.1586/14737175.7.10.1417.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Belda-Lois JM, Mena-del Horno S, Bermejo-Bosch I, Moreno JC, Pons JL, Farina D, et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil. 2011;8(1):66. https://doi.org/10.1186/1743-0003-8-66.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Obembe AO, Olaogun MO, Adedoyin R. Gait and balance performance of stroke survivors in South-Western Nigeria-a cross-sectional study. Pan Afr Med J. 2014;17(Suppl 1):6. https://doi.org/10.11694/pamj.supp.2014.17.1.3001.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hamzat TK, Olaleye OA. Stroke rehabilitation: when should ambulation commence. J Nigeria Med Rehabil Ther. 2002;7(20):23–5.

    Google Scholar 

  17. Baer G, Smith M. The recovery of walking ability and subclassification of stroke. Physiother Res Int. 2001;6(3):135–44. https://doi.org/10.1002/pri.222.

    Article  CAS  PubMed  Google Scholar 

  18. Preston E, Ada L, Dean CM, Stanton R, Waddington G. What is the probability of patients who are nonambulatory after stroke regaining independent walking? A systematic review. Int J Stroke. 2011;6(6):531–40. https://doi.org/10.1111/j.1747-4949.2011.00668.x.

    Article  PubMed  Google Scholar 

  19. Jørgensen L, Jacobsen BK, Wilsgaard T, Magnus JH. Walking after stroke: does it matter? Changes in bone mineral density within the first 12 months after stroke. A longitudinal study. Osteoporos Int. 2000;11(5):381–7. https://doi.org/10.1007/s001980070103.

    Article  PubMed  Google Scholar 

  20. Kongsawasdi S, Klaphajone J, Watcharasaksilp K, Wivatvongvana P. Prognostic factors of functional recovery from left hemispheric stroke. Scientific World Journal. 2018;2018:4708230. https://doi.org/10.1155/2018/4708230.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hsieh CL, Sheu CF, Hsueh IP, Wang CH. Trunk control as an early predictor of comprehensive activities of daily living function in stroke patients. Stroke. 2002;33(11):2626–30. https://doi.org/10.1161/01.str.0000033930.05931.93.

    Article  PubMed  Google Scholar 

  22. Kwakkel G, Kollen B, Lindeman E. Understanding the pattern of functional recovery after stroke: facts and theories. Restor Neurol Neurosci. 2004;22(3-5):281–99.

    PubMed  Google Scholar 

  23. Lee KB, Lim SH, Kim KH, Kim KJ, Kim YR, Chang WN, et al. Six-month functional recovery of stroke patients: a multi-time-point study. Int J Rehabil Res. 2015;38(2):173. https://doi.org/10.1097/mrr.0000000000000108.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Paker N, Buğdaycı D, Tekdöş D, Kaya B, Dere Ç. Impact of cognitive impairment on functional outcome in stroke. Stroke Res Treat. 2010;652612:5. https://doi.org/10.4061/2010/652612.

    Article  Google Scholar 

  25. Goh LY, Tan IO, Yang LC, Ng SS. Effects of cognitive and motor tasks on the walking speed of individuals with chronic stroke. Medicine. 2017;96(9):e6232. https://doi.org/10.1097/md.0000000000006232.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Charan J, Biswas T. How to calculate sample size for different study designs in medical research? Indian J Psychol Med. 2013;35(2):121–6.

    Article  Google Scholar 

  27. Mehrholz J, Wagner K, Rutte K, Meiβner D, Pohl M. Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke. Arch Phys Med Rehabil. 2007;88(10):1314–9. https://doi.org/10.1016/j.apmr.2007.06.764.

    Article  PubMed  Google Scholar 

  28. Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7(1):13.

    CAS  PubMed  Google Scholar 

  29. Chen KL, Chen CT, Chou YT, Shih CL, Koh CL, Hsieh CL. Is the long form of the Fugl-Meyer motor scale more responsive than the short form in patients with stroke? Arch Phys Med Rehabil. 2014;95(5):941–9. https://doi.org/10.1016/j.apmr.2014.01.014.

    Article  PubMed  Google Scholar 

  30. Fulk GD, Reynolds C, Mondal S, Deutsch JE. Predicting home and community walking activity in people with stroke. Arch Phys Med Rehabil. 2010;91(10):1582–6. https://doi.org/10.1016/j.apmr.2010.07.005.

    Article  PubMed  Google Scholar 

  31. Verheijde JL, White F, Tompkins J, Dahl P, Hentz JG, Lebec MT, et al. Reliability, validity, and sensitivity to change of the lower extremity functional scale in individuals affected by stroke. PM R. 2013;5(12):1019–25. https://doi.org/10.1016/j.pmrj.2013.07.001.

    Article  PubMed  Google Scholar 

  32. Asano M, Miller WC, Eng JJ. Development and psychometric properties of the ambulatory self-confidence questionnaire. Gerontology. 2007;53(6):373–81. https://doi.org/10.1159/000104830.

    Article  PubMed  Google Scholar 

  33. Liaw LJ, Hsieh CL, Lo SK, Lee S, Huang MH, Lin JH. Psychometric properties of the modified Emory Functional Ambulation Profile in stroke patients. Clin Rehabil. 2006;20(5):429–37. https://doi.org/10.1191/0269215506cr950oa.

    Article  PubMed  Google Scholar 

  34. Lennon S, Johnson L. The modified Rivermead mobility index: validity and reliability. Disabil Rehabil. 2000;22(18):833–9. https://doi.org/10.1080/09638280050207884.

    Article  CAS  PubMed  Google Scholar 

  35. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142–8. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x.

    Article  CAS  PubMed  Google Scholar 

  36. Berg KO, Wood-Dauphinee SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992;83(Suppl 2):S7–11.

    PubMed  Google Scholar 

  37. Teng E, Chui H. The modified mini-mental state examination (3MS). Can J Psychiatry. 1987;41(2):114–21.

    Google Scholar 

  38. Wilkinson MJ, Menz HB, Raspovic A. The measurement of gait parameters from footprints. Foot. 1995;5(2):84–90.

    Article  Google Scholar 

  39. Badaru UM, Alonge V, Adeniyi AF, Ogwumike OO. Relative therapeutic efficacy of the treadmill and step bench in gait rehabilitation of hemiparetic stroke patients. Afr J Physiother Rehabil Sci. 2012;4(1-2):45–50.

    Google Scholar 

  40. Handa T, Sahara R, Yoshizaki K, Endou T, Utsunomiya M, Kuroiwa C, et al. Examination of reliability and validity of walking speed, cadence, stride length-comparison of measurement with stopwatch and three-dimension motion analyzer. J Phys Ther Sci. 2007;19(4):213–22.

    Article  Google Scholar 

  41. Graham JE, Ostir GV, Fisher SR, Ottenbacher KJ. Assessing walking speed in clinical research: a systematic review. J Eval Clin Pract. 2008;14(4):552–62.

    Article  Google Scholar 

  42. Danielsson A, Willén C, Sunnerhagen KS. Is walking endurance associated with activity and participation late after stroke?? Disabil Rehabil. 2011;33(21-22):2053–7. https://doi.org/10.3109/09638288.2011.560329.

    Article  PubMed  Google Scholar 

  43. Coleman ER, Moudgal R, Lang K, Hyacinth HI, Awosika OO, Kissela BM, et al. Early rehabilitation after stroke: a narrative review. Curr Atheroscler Rep. 2017;19(12):59. https://doi.org/10.1007/s11883-017-0686-6.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Smith MC, Barber PA, Stinear CM. The TWIST algorithm predicts time to walking independently after stroke. Neurorehabil Neural Repair. 2017;31(10-11):955–64. https://doi.org/10.1177/1545968317736820.

    Article  PubMed  Google Scholar 

  45. Pohl PS, Perera S, Duncan PW, Maletsky R, Whitman R, Studenski S. Gains in distance walking in a 3-month follow-up poststroke: what changes? Neurorehabil Neural Repair. 2004;18(1):30–6. https://doi.org/10.1177/0888439003260494.

    Article  PubMed  Google Scholar 

  46. Mahendran N, Kuys SS, Brauer SG. Recovery of ambulation activity across the first six months post-stroke. Gait Posture. 2016;49:271–6. https://doi.org/10.1016/j.gaitpost.2016.06.038.

    Article  PubMed  Google Scholar 

  47. Fulk GD, He Y, Boyne P, Dunning K. Predicting home and community walking activity poststroke. Stroke. 2017;48(2):406–11. https://doi.org/10.1161/strokeaha.116.015309.

    Article  PubMed  Google Scholar 

  48. Jørgensen HS, Nakayama H, Raaschou HO, Vive-Larsen J, Støier M, Olsen TS. Outcome and time course of recovery in stroke. Part I: outcome. The Copenhagen Stroke Study. Arch Phys Med Rehabil. 1995;76(5):399–405. https://doi.org/10.1016/s0003-9993(95)80567-2.

    Article  PubMed  Google Scholar 

  49. Jang SH. The recovery of walking in stroke patients: a review. Int J Rehabil Res. 2010;33(4):285–9. https://doi.org/10.1097/mrr.0b013e32833f0500.

    Article  PubMed  Google Scholar 

  50. Ichinosawa Y, Shimizu S, Takemura N, Taira K, Hamakawa M, Nakachi Y, et al. Gait speed and balance function strongly determine the ability to walk independently without using a wheelchair in a facility setting for stroke patients. Kitasato Med J. 2018;48(1):16–25.

    Google Scholar 

  51. Rosa MC, Marques A, Demain S, Metcalf CD. Fast gait speed and self-perceived balance as valid predictors and discriminators of independent community walking at 6 months post-stroke–a preliminary study. Disabil Rehabil. 2015;37(2):129–34. https://doi.org/10.3109/09638288.2014.911969.

    Article  PubMed  Google Scholar 

  52. Kluding P, Gajewski B. Lower-extremity strength differences predict activity limitations in people with chronic stroke. Phys Ther. 2009;89(1):73–81. https://doi.org/10.2522/ptj.20070234.

    Article  PubMed  Google Scholar 

  53. Grau-Pellicer M, Chamarro-Lusar A, Medina-Casanovas J, Serdà Ferrer BC. Walking speed as a predictor of community mobility and quality of life after stroke. Top Stroke Rehabil. 2019;26(5):349–58. https://doi.org/10.1080/10749357.2019.1605751.

    Article  PubMed  Google Scholar 

  54. Bijleveld-Uitman M, van de Port I, Kwakkel G. Is gait speed or walking distance a better predictor for community walking after stroke? J Rehabil Med. 2013;45(6):535–40. https://doi.org/10.2340/16501977-1147.

    Article  PubMed  Google Scholar 

  55. An S, Lee Y, Shin H, Lee G. Gait velocity and walking distance to predict community walking after stroke. Nurs Health Sci. 2015;17(4):533–8. https://doi.org/10.1111/nhs.12234.

    Article  PubMed  Google Scholar 

  56. Amatachaya S, Chuadthong J, Thaweewannaku T, Srisim K, Phonthee S. Levels of community ambulation ability in patients with stroke who live in a rural area. Malays J Med Sci. 2016;23(1):56.

    PubMed  PubMed Central  Google Scholar 

  57. van Meulen FB, Weenk D, van Asseldonk EH, Schepers HM, Veltink PH, Buurke JH. Analysis of balance during functional walking in stroke survivors. PloS one. 2016;11(11):e0166789. https://doi.org/10.1371/journal.pone.0166789.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hessam M, Salehi R, Yazdi MJ, Negahban H, Rafie S, Mehravar M. Relationship between functional balance and walking ability in individuals with chronic stroke. J Phys Ther Sci. 2018;30(8):993–6. https://doi.org/10.1589/jpts.30.993.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Michael KM, Allen JK, Macko RF. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil. 2005;86(8):1552–6. https://doi.org/10.1016/j.apmr.2004.12.026.

    Article  PubMed  Google Scholar 

  60. Plummer-D'Amato P, Altmann LJ, Behrman AL, Marsiske M. Interference between cognition, double-limb support, and swing during gait in community-dwelling individuals poststroke. Neurorehabil Neural Repair. 2010;24(6):542–9. https://doi.org/10.1177/1545968309357926.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Awad LN, Binder-Macleod SA, Pohlig RT, Reisman DS. Paretic propulsion and trailing limb angle are key determinants of long-distance walking function after stroke. Neurorehabil Neural Repair. 2015;29(6):499–508. https://doi.org/10.1177/1545968314554625.

    Article  PubMed  Google Scholar 

  62. Balasubramanian CK, Neptune RR, Kautz SA. Variability in spatiotemporal step characteristics and its relationship to walking performance post-stroke. Gait Posture. 2009;29(3):408–14. https://doi.org/10.1016/j.gaitpost.2008.10.061.

    Article  PubMed  Google Scholar 

  63. Sagnier S, Renou P, Olindo S, Debruxelles S, Poli M, Rouanet F, et al. Gait change is associated with cognitive outcome after an acute ischemic stroke. Front Aging Neurosci. 2017;9:153. https://doi.org/10.3389/fnagi.2017.00153.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Tertiary Education Trust Fund (TETFund) Nigeria and the Management of Bayero University, Kano, for sponsoring and facilitating the sponsorship of this study respectively.

Funding

The study was supported by a Postgraduate Study Fellowship/Scholarship (via Bayero University, Kano) by the Tertiary Education Trust Fund (TETFund), Nigeria.

Author information

Authors and Affiliations

Authors

Contributions

JSU, CAOG, and OAO conceptualize the study, design the study, and wrote the paper. JSU performed the data collection. JSU and CAO analyzed the data. JSU, CAO, and OAO interpreted the data. All the authors were substantially involved in drafting and revising of the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Jibrin Sammani Usman.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Health Research Ethics Committees of College of Medicine University of Lagos, Aminu Kano Teaching Hospital, and Kano State Ministry of Health. All participants consented to participate in the study before being enrolled in the study.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Usman, J.S., Gbiri, C.A.O. & Olawale, O.A. Predictors of ambulatory recovery and walking proficiency in community-dwelling stroke survivors: a cross-sectional study. Bull Fac Phys Ther 27, 38 (2022). https://doi.org/10.1186/s43161-022-00097-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43161-022-00097-5

Keywords

  • Ambulatory recovery
  • Walking proficiency
  • Community-dwelling
  • Stroke survivors
  • Predictors