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. 2022 Nov 21;80(7):681–688. doi: 10.1055/s-0042-1755267

Predictors of functional communication in people with aphasia after stroke

Preditores de comunicação funcional em pessoas com afasia após acidente vascular cerebral

Adriana Fernandes 1, Helena Fraga-Maia 2, Iara Maso 1,3, Isabela Guimarães Matos 1, Lene Gomes 1, Matheus Matos 1, Andressa Santana 1, Jamary Oliveira-Filho 4, Pedro Antônio de Jesus 3,4, Elen Beatriz Pinto 1,2
PMCID: PMC9685830  PMID: 36254440

Abstract

Background

Aphasia, the most common language disorder secondary to stroke, has been associated with increased mortality, longer hospitalization and rehabilitation times, worse performance in daily activities, increased financial burden, and short- and long-term complications. Aphasia can negatively impact functional communication skills, including social networks, social activities, relationships with other people and social support.

Objective

To evaluate patients with poststroke aphasia in their respective residences to investigate potential predictors of functional communication.

Methods

The prospective cohort included patients with poststroke aphasia aged 18 years or older who resided in the city of Salvador, Northeastern Brazil. Following discharge from the Stroke Unit (SU), the individuals themselves, or their guardians, were contacted by telephone to schedule a home visit no less than three months after discharge. At baseline, sociodemographic and clinical data were collected, in addition to the scores on the National Institutes of Health Stroke Scale (NIHSS) and modified Barthel Index (mBI). The American Speech-Language-Hearing Association Functional Assessment of Communication Skills for Adults (ASHA FACS) was applied at the patients’ homes. Multivariate linear regression was employed using the total score on the ASHA FACS as the outcome of interest.

Results

A multivariate analysis of the associated factors identified using the linear regression revealed that only functional capacity (as assessed by the mBI) upon discharge from the SU remained as an independent predictor of functional communication performance (β = 0.042; 95% confidence interval [95%CI] = 0.013–0.071; p = 0.002).

Conclusion

The functional capacity to perform daily activities, evaluated upon discharge from a stroke unit, was identified as a potential predictor of functional communication performance, regardless of the time elapsed after the stroke.

Keywords: Stroke, Aphasia, Communication, Community Integration

INTRODUCTION

Aphasia, the most common language disorder secondary to stroke, has been associated1 with increased mortality, longer hospitalization and rehabilitation times, worse performance in daily activities, increased financial burden, and short- and long-term complications. This condition has been shown2 to compromise functional recovery and the social reintegration of affected individuals.

People with aphasia present alterations in the content, form and use of language, which manifest both in the expression and interpretive aspects of their abilities, and can range from partial to complete impairment.3 Along with the linguistic components of communication, other domains, such as attention, memory, executive function, and visuospatial abilities may be affected,4 in addition to emotional aspects.5 Aphasia can negatively impact functional communication skills, including the social networks, social activities, relationships with other people, and social support.6

Studies7,8 on early recovery and functional reorganization have considered distinct prognostic factors that may influence the course of long-term recovery in people with aphasia. The lack of concordant data in the literature may be due to the heterogenous characteristics of the studied individuals, as well as the methodology employed, which could significantly influence the patterns of communicative recovery poststroke.8 Researchers9 have acknowledged difficulties in predicting recovery outcomes in study populations, especially due to the classification of the severity of the aphasia of the included individuals. Sociodemographic factors and others related to education and income, as well as stroke severity, can play crucial roles in the readaptation of the individuals to their domiciles and functional communication.10,11

Different studies9,12 have sought to expand the scope of the investigation into the identification of predictors of recovery, in distinct poststroke phases and contexts in people with aphasia. Data12, 13, 14, 15 has indicated that functional communication performance in these persons is not only linked to stroke severity, but is also dependent on the characteristics of the individuals, their previous linguistic abilities, occupation, cultural interests, and level of schooling. Functional communication is considered the ability to express and interpret messages, and to communicate effectively and independently in conformance with the environmental context.16 To provide greater insight beyond linguistic behavior, the American Speech-Language-Hearing Association Functional Assessment of Communication Skills for Adults (ASHA FACS)17,18 was developed to expand the capacity to evaluate communicative abilities in each individual’s respective environment, providing information on their ability to perform basic daily communication tasks, considering compensative measures, adaptations, and the time required to communicate.

In an effort to amplify our understanding with regard to the impacts of aphasia and enhance the precision in prognosis and intervention measures designed to improve communication performance inpeople with aphasia after stroke, the most affected communication abilities have been documented,19 and potential indicators of communication recovery have been investigated.7,12,13 Considering that few investigations20, 21, 22, 23 have focused on functional communication abilities developed by these patients in their natural environment, the present study aimed to evaluate people with aphasia after stroke in this context to identify potential predictors that could influence the performance of functional communication.

METHODS

Design

The present study employed a prospective cohort developed as part of a previous study.24

Participants

The present study included individuals aged 8 years or older who received a diagnosis of ischemic stroke confirmed by neuroimaging (computed tomography or magnetic resonance imaging) and resided in the city of Salvador (state of Bahia, Northeastern Brazil). Following discharge from the Stroke Unit (SU) of a public hospital, the individuals themselves, or their guardians,were contactedtoschedule a home visit no less than three months after discharge. At each visitation, a speech-language therapist (SLT) conducted clinical assessments of language function to confirm the diagnosis of aphasia. Deceased individuals and those who experienced a new episode of stroke were excluded.

Data collection procedures

Baseline data was obtained from a previous prospective cohort study.24 Further data collection was performed via in-home visitations no less than three months after discharge, scheduled by telephone according to each participant’s home address for purposes of optimization. The visitations took place between September 2016 and May 2018, involving an SLT and a physiotherapist who were both specialists in neurorehabilitation. The present research protocol was approved by the Institutional Review Board, and all participants or guardians signed a informed consent form.

Collection instruments and definition of variables during hospitalization

The sociodemographic data was comprised of age, sex, level of schooling, self-reported skin color (which, for the purpose of analysis, was summarized as white/non-white), marital status (married or unmarried), family income, expressed in terms of multiples of monthly minimum wage (1 monthly minimum wage = R$ 954; roughly US$ 250). Additional data parameters included current smoking habit and alcohol consumption.

The clinical characteristics of the stroke event were described in accordance with involvement of the left middle cerebral artery, hemorrhagic transformation, previous occurrence of stroke, and thrombolytic treatment. The severity of the stroke was measured at the SU using the National Institutes of Health Stroke Scale (NIHSS),25 with higher scores indicating greater severity (range: 0–42). Individuals who presented language impairment according to item 9 of theNIHSSwere considered aphasic and markedfor follow-up by an SLT during the in-home visitations.

The length of hospitalization was defined in accordance with the number of days in the SU. The following comorbidities were considered in the medical records: hypertension (HTN), diabetes mellitus (DM), hypercholesterolemia, atrial fibrillation (AF), and other cardiopathies. The modified Barthel Index (mBI) was used to assess functional capacity in daily activities upon hospital discharge.

The results were categorized into functionality groups: 50–total independence; 46 to 49–slightly dependent; 31 to 45–moderate dependence; 11 to 30–important dependence; and 0 to 10–total dependence.25

Collection instruments and definition of variables during home visits

The patients identified with language impairment during hospitalization were subsequently evaluated by an SLT in their homes. The functional diagnosis of aphasia involved clinical assessments of language, including spontaneous speech (directed and non-directed), auditory and written comprehension, repetition, reading, and writing.26

To evaluate the functional communication in aphasic individuals, the ASHA FACS,24 an instrument to assess verbal, written and non-verbal expression skills, numerical concepts, and the necessary comprehension for efficient communication, was applied. It consists of 43 questions divided into 4 domains: Social Communication; Communication of Basic Needs; Reading, Writing, and Number Concepts; and Daily Planning. The ASHA FACS is used to obtain parameters related to communicative activities in persons with difficulties in functional communication. The version of this instrument validated in Brazil was applied by the SLT at each patient’s home, considering colleted information from the individual, or relative or caregiver.27

The maximum possible score on the ASHA FACS is 7, which indicates that a patient is totally independent with regard to communicative behavior; 6 indicates the patient requires assistance in rare cases; 5, that the patient occasionally needs assistance; 4 indicates that an individual often requires assistance; 3, that the patient very frequently needs assistance; 2, that the patient requires continual assistance to perform a communicative behavior;and1, that the patient is not able to perform any communicative activity.28,29

Statistical analysis

In the descriptive analysis, the numerical variables were expressed as means and standard deviations or medians and interquartile ranges (IQRs), while the categorical variables were expressed as absolute and relative frequencies.

Univariate analysis using linear regression was applied to assess the influence of the variables identified in the literature and clinical practice (such as age, sex, level of schooling, stroke severity, time elapsed since the stroke, poststroke functional capacity, marital status, previous stroke event, and other clinical aspects) on the outcome of interest, the total score on the ASHA FACS.

Variables indicating associations with functional communication in the univariate analysis (p <0.10), as well as the vascular territory of the lesion, were inserted into a multivariate linear regression model. The results of the regression model were expressed using the β coefficient and the respective confidence intervals.

RESULTS

From a total of 204 individuals evaluated between January 2015 and June 2016, 81 people with aphasia were identified. Of these, 38 were excluded (14 had died, 13 were lost to follow-up, 7 presented new episodes of stroke, and 4 refused to take part in the research). Thus, 43 individuals were subsequently visited by the research team in their homes following discharge from the SU. After discharge, 12 individuals (27.9%) underwent speech therapy and 11 (25.6%) underwent physical therapy. We found that only 4 individuals (9.3%) continued to perform their work activities.

Table 1 details the characterization of the sample: 26 (60.5%) were female patients with a median (IQR) age of 66 years (range: 57 to 70 years); 35 (81.4%) self-reported their skin color as non-white, and 22 (51.2%) were married or in a stable relationship. the median (IQR) years of schooling were 5 (range: 4 to 12 years), and the median (IQR) family income was 2 (range: 1 to 2) monthly minimum wages.

Table 1. Sociodemographic, clinical and functional characteristics of the study sample.

Age at time of admission to the Stroke Unit: median (IQR) 66 (57–70)
Females: n (%) 26 (60.5)
Non-white skin color: n (%) 35 (81.4)
Married or in a stable relationship: n (%) 22 (51.2)
Years of schooling: median (IQR) 5 (4–12)
Family income*: median (IQR) 1 (1–2)
Alcohol consumption: n (%) 7 (16.3)
Current smoking habit: n (%) 6 (14.0)
Previous stroke: n (%) 13 (30.2)
Comorbidities Systemic arterial hypertension: n (%) 32 (74.4)
Diabetes mellitus: n (%) 18 (41.9)
Obesity: n (%) 5 (11.6)
Hypercholesterolemia: n (%) 3 (7.0)
Atrial fibrillation: n (%) 7 (16.3)
Other cardiopathies: n (%) 11 (25.6)
Stroke severity (NIHSS): median (IQR) 12 (5–19)
Left middle cerebral artery involvement: n (%) 33 (76.7)
Hemorrhagic transformation: n (%) 5 (11.6)
Thrombolytic treatment: n (%) 18 (41.9)
Length of stay in Stroke Unit in days: mean (SD) 8.7 (± 4.3)
Functional capacity (mBI) score upon discharge from the Stroke Unit: median (IQR) 46 (26–50)

Abbreviations: IQR, interquartile range; NIHSS, National Institutes of Health Stroke Scale; mBI, modified Barthel Index; SD, standard deviation.

Note: *Family income expressed as the number of monthly minimum wages in Brazilian Reais (1 minimum wage is roughly US$ 200).

The most prevalent comorbidity identified was HTN 32 (74.4%), followed by DM 18 (41.9%) and other cardiopathies 11 (25.6%). Regarding lifestyle habits prior to the stroke event, 7 (16.3%) reported consumption of alcoholic beverages, and 6 (14.0%) were current smokers. Prevoious stroke event was reported by 13 (30.2%), and 33 (76.7%) presented involvement of the left middle cerebral artery. The median (IQR) NIHSS score was of 12 (range: 5 to 19), indicating moderate to serious neurological impairment. The average length of stay in the SU was of 8.7 days (± 4.3 days), and 41.9% (18/43) of the patients had been submitted to thrombolytic treatment. Upon discharge from the SU, functional capacity assessments by mBI presented a median score (IQR) of 46 (range: 26 to 50), classified as slightly dependent.

Following discharge from the SU, the patients were assessed in their homes, and the mean time since the stroke was of 18.6 (± 6.0) months. The average score on the ASHA FACS was 4.9 (range: 3.8 to 5.9), indicating a patient’s need for very frequent to occasional assistance. When analyzed by domain, the median (IQR) of the Communication of Basic Needs domain was 6.7 (range: 6.0 to 7.0), suggesting that the patients rarely needed assistance, or were totally independent, in this domain. The median (IQR) of the Social Communication domain was 6.4 (4.6 to 6.9), revealing that individuals needed minimal assistance or were almost independent. However, the median (IQR) of the Reading, Writing, and Number Concepts domain was 3.8 (2.0 to 5.6), indicating the need for very frequent assistance, and the median (IQR) of the Daily Planning domain was 3.4 (1.8 to 4.8), suggesting that patients needed frequent assistance in this domain (►Figure 1).

Figure 1. Distribution of ASHA FACS domain scores among the study sample.

Figure 1

Table 2 details the factors identified in the univariate analysis as associated with functional communication performance. Linear regression was performed considering the total score on the ASHA FACS as the outcome of interest. The following variables were identified as associated with functional communication: level of schooling (p = 0.111; 95% confidence interval [95%CI] = 0.025 to 0.196; p = 0.012), family income (β = 0.249; 95%CI = 0.29 to 0.468; p = 0.027), and functional capacity (according to the mBI) upon discharge from the SU (β = 0.055; 95%CI = 0.025 to 0.85; p = 0.001).

Table 2. Univariate linear regression analysis of factors associated with functional communication in poststroke aphasic individuals.

Variable β (95%CI) p
Age at time of admission to the Stroke Unit -0.011 (-0.45–0.22) 0.508
Level of schooling (in years) 0.111 (0.025–0.196) 0.012
Family income 0.249 (0.29–0.468) 0.027
Stroke severity (NIHSS) -0.012 (-0.081–0.057) 0.730
Length of hospitalization at the Stroke Unit (in days) -0.003 (-0.113–0.106) 0.952
Time since stroke (in months) 0.035 (-0.043–0.113) 0.317
Functional capacity (mBI) upon discharge from the Stroke Unit 0.055 (0.025–0.85) 0.001
Female sex -0.161 (-1.107–0.789) 0.733
Non-white skin color -0.149 (-1.339–1.041) 0.801
Married or in a stable relationship 0.020 (-0.907–0.948) 0.965
Previous stroke -0.803 (-1.780–0.174) 0.105
Left middle cerebral artery involvement -0.923 (-1.981 - 0.135) 0.085
Hemorrhagic transformation 0.343 (-1.098 - 1.785) 0.633
Thrombolytic treatment 0.516 (-0.410 - 1.441) 0.267
Actively employed poststroke 1.203 (-0.346 - 2.753) 0.125
Active employment prior to thr stroke event -0.436 (-1.524 - 0.652) 0.423

Abbreviations: 95%CI, 95% confidence interval; NIHSS, National Institutes of Health Stroke Scale; mBI, modified Barthel Index.

Note: *Family income expressed as the number of monthly minimum wages in Brazilian Reais (1 minimum wage is roughly US$ 200).

The subsequent multivariate analysis of the results identified using linear regression revealed that only functional capacity (according to the mBI) after discharge from the SU remained as an independent predictor of functional communication ability (β = 0.042; 95%CI = 0.013 to 0.071; p = 0.002) (►Table 3).

Table 3. Multivariate linear regression analysis of predictor of functional communication in poststroke aphasic individuals.

Variable β (95%CI) p
Level of schooling (in years) 0.071 (-0.005–0.146) 0.060
Family income 0.118 (-0.076–0.312) 0.221
Left middle cerebral artery involvement -0.582 (-1.455–0.291) 0.253
Functional capacity (mBI) score upon discharge from the Stroke Unit 0.042 (0.013–0.071) 0.002

Abbreviations: 95%CI, 95% confidence interval; mBI, modified Barthel Index.

Note: *Family income expressed as the number of monthly minimum wages in Brazilian Reais (1 minimum wage is roughly US$ 200). R2 0.388.

DISCUSSION

The present study found that functional communication in patients with aphasia investigated in a previous cohort study24 was associated with functional capacity in the performance of daily activities, as evaluated upon discharge from a severe SU. Data in the literature indicate that aphasia is strongly associated with functional capacity in multiple aspects,1,30 which impacts the performance of daily activities,31 as well as an individual’s home integration and social reintegration.2,32 In line with our results, another study12 also showed that the results on the mBI enabled the prediction of the functional impacts of aphasia in individuals after a stroke.

Previous studies30,33 that sought to investigate the relationship between aphasia and motor performance reported that the discordant results obtained need further investigation. An intensive neurorehabilitation program in people with aphasia resulted in functional improvement in some aspects, such as, ambulation (chair/bed transfer abilities); however little improvement was observed in terms of daily care activities, such as bathing, dressing, using the bathroom, as well as climbing stairs.30 This provides evidence of the complexity of these activities, which are not only associated with alterations in the motor ability of the limbs and trunk, but alsoimplycognitive demands32,33 relatedtotheplanning and control of actions involved in complex motor sequences, mainly those pertaining to the upper right limb, which is more commonly used in the performance of daily activities by right-handed individuals.34,35 Although the present study did not attempt to stratify the performance of activities in each of the mBI domains, we did observe that the communicative performance of the included individuals was more efficient in the execution of more concrete or simple tasks than in those requiring additional linguistic components and abstract reasoning.36

Our analysis of functional communication results distributed across the ASHA FACS domains indicate that the studied individuals presented higher scores on the Communication of Basic Needs domain. These activities tend to be more related to elements of daily communication, in which oral verbal expression is generally not required.17 As people with aphasia tend to communicate most often with relatives or caregivers, who may become the individual’s main speakers, difficulties can arise when faced with the need to communicate with strangers or in communicative interactions involving more complex or abstract subjects.19

An individual’s level of schooling is known to affect both recovery and language performance poststroke.37,38 Individuals with low levels of schooling employ simpler grammar structures and use more oral than written language, and they do not engage as much in social reading and writing activities.38,39 The results obtained herein may be associated with the level of schooling of the sample; indeed, in Brazil, much of the population suffers from limited access to formal education.

STUDY LIMITATIONS AND FUTURE RESEARCH

The present results are strengthened by the fact that the functional communication assessments were performed during home visits, that is, in the very environment in which the individuals interact, which enabled us to more robustly investigate their functional communicative performance. However, we recognize as limitations the impossibility of including, in the baseline data collected during the hospital stay, information regarding cognitive functioning and the initial severity of the aphasia. The absence of such data may have impacted the investigation of predictors of functional communication performance. For future research, the inclusion of such aspects can help to expand knowledge about the influence of cognitive functioningon functional capacityand functional communication of individuals with aphasia after stroke. In conclusion, the functional capacity assessed by the mBI at discharge from the SU was identified as a potential predictor of the performance of functional communication, regardless of the time elapsed since the stroke.

IMPLICATIONS FOR REHABILITATION

  1. The ability to perform daily activities as assessed by the mBI, identified as a predictor of functional communication, can aid in planning multidisciplinary rehabilitative approaches aimed at promoting independence and functional communication in people with aphasia after stroke.

  2. The characterization of skills developed as strategies for functional communication in the homes of poststroke aphasic patients expands on the body of investigation on language impairment, and can complement the existing clinical measures and other assessments of language skills and deficits.

Acknowledgments

The authors would like to thank Universidade do Estado da Bahia (UNEB) for its support to data collection in the home visits by providing an institutional car and driver. We would also like to thank Andris K. Walter for the critical review of the manuscript and copyediting services regarding the English content.

References

  • 1.Lazar RM, Boehme AK. Aphasia as a predictor of stroke outcome. Curr Neurol Neurosci Rep. 2017;17(11):1–5. doi: 10.1007/s11910-017-0797-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Dalemans RJP, De Witte LP, Beurskens AJHM, Van Den Heuvel WJ, Wade DT. An investigation into the social participation of stroke survivors with aphasia. Disabil Rehabil. 2010;32(20):1678–1685. doi: 10.3109/09638281003649938. [DOI] [PubMed] [Google Scholar]
  • 3.Sinanović O, Mrkonjić Z, Zukić S, Vidović M, Imamović K. Post-stroke language disorders. Acta Clin Croat. 2011;50(01):79–94. doi: 10.3109/09638281003649938. [DOI] [PubMed] [Google Scholar]
  • 4.El Hachioui H, Visch-Brink EG, Lingsma HF, et al. Nonlinguistic cognitive impairment in poststroke aphasia: a prospective study. Neurorehabil Neural Repair. 2014;28(03):273–281. doi: 10.1177/1545968313508467. [DOI] [PubMed] [Google Scholar]
  • 5.Cahana-Amitay D, Albert ML, Pyun SB, et al. Language asaStressor in Aphasia. Aphasiology. 2011;25(02):593–614. doi: 10.1080/02687038.2010.541469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hilari K, Needle JJ, Harrison KL. What are the important factors in health-related quality of life for people with aphasia? A systematic review. Arch Phys Med Rehabil. 2012;93(1) Suppl:S86–S95. doi: 10.1016/j.apmr.2011.05.028. [DOI] [PubMed] [Google Scholar]
  • 7.Wilson SM, Eriksson DK, Brandt TH, et al. Patterns of recovery from aphasia in the first 2 weeks after stroke. J Speech Lang Hear Res. 2019;62(03):723–732. doi: 10.1044/2018_JSLHR-L-18-0254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Holland A, Fromm D, Forbes M, MacWhinney B. Long-term Recovery in Stroke Accompanied by Aphasia: A Reconsideration. Aphasiology. 2017;31(02):152–165. doi: 10.1080/02687038.2016.1184221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hillis AE, Beh YY, Sebastian R, et al. Predicting recovery in acute poststroke aphasia. Ann Neurol. 2018;83(03):612–622. doi: 10.1002/ana.25184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Nakagawa Y, Sano Y, Funayama M, Kato M. Prognostic factors for long-term improvement from stroke-related aphasia with adequate linguistic rehabilitation. Neurol Sci. 2019;40(10):2141–2146. doi: 10.1007/s10072-019-03956-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hilari K, Byng S. Health-related quality of life in people with severe aphasia. Int J Lang Commun Disord. 2009;44(02):193–205. doi: 10.1080/13682820802008820. [DOI] [PubMed] [Google Scholar]
  • 12.El Hachioui H, Lingsma HF, vande Sandt-Koenderman MW, Dippel DWJ, Koudstaal PJ, Visch-Brink EG. Long-term prognosis of aphasia after stroke. J Neurol Neurosurg Psychiatry. 2013;84(03):310–315. doi: 10.1136/jnnp-2012-302596. [DOI] [PubMed] [Google Scholar]
  • 13.Worrall LE, Hudson K, Khan A, Ryan B, Simmons-Mackie N. Determinants of living well with aphasia in the first year post-stroke: a prospective cohort study. Arch Phys Med Rehabil. 2017;98(02):235–240. doi: 10.1016/j.apmr.2016.06.020. [DOI] [PubMed] [Google Scholar]
  • 14.Kjellén E, Laakso K, Henriksson I. Aphasia and literacy-the insider’s perspective. Int J Lang Commun Disord. 2017;52(05):573–584. doi: 10.1111/1460-6984.12302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Soares EC, Ortiz KZ. Influence of schooling on language abilities of adults without linguistic disorders. Sao Paulo Med J. 2009;127(03):134–139. doi: 10.1590/S1516-31802009000300005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.American Speech-Language-Hearing Association (ASHA) Advisory Report, Functional Communication Measures Project. Rock-ville, MD: ASHA; 1990. [Google Scholar]
  • 17.Frattali C, Thompson CK, Holland AL, Wohl CB, Ferketic M. Functional Assessment of Communication Skills for Adult. Rock-ville, MD: American Speech-Language-Hearing Association; 1995. [Google Scholar]
  • 18.Donovan NJ, Rosenbek JC, Ketterson TU, Velozo CA. Adding meaning to measurement: Initial Rasch analysis of the ASHA FACS Social Communication Subtest. Aphasiology. 2006;20:2–4. doi: 10.1080/02687030500475184. 362–373. [DOI] [Google Scholar]
  • 19.Mazaux JM, Lagadec T, de Sèze MP, et al. Communication activity in stroke patients with aphasia. J Rehabil Med. 2013;45(04):341–346. doi: 10.2340/16501977-1122. [DOI] [PubMed] [Google Scholar]
  • 20.Davidson B, Worrall L, Hickson L. Identifying the communication activities ofolder people with aphasia: Evidence from naturalistic observation. Aphasiology. 2003;17(03):243–264. doi: 10.1080/729255457. [DOI] [Google Scholar]
  • 21.Ross KB, Wertz RT. Accuracy of formal tests for diagnosing mild aphasia: An application of evidence-based medicine. Aphasiology. 2004;18:337–355. doi: 10.1080/02687030444000002. [DOI] [Google Scholar]
  • 22.Meier EL, Johnson JP, Villard S, Kiran S. Does naming therapy make ordering in a restaurant easier? Dynamics of co-occurring change in cognitive-linguistic and functional communication skills in aphasia. Am J Speech Lang Pathol. 2017;26(02):266–280. doi: 10.1044/2016_AJSLP-16-0028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Rangamani GN, Judovsky HM. Quality of communication life in people with aphasia: implications for intervention. Ann Indian Acad Neurol. 2020;23(Suppl 2):S156–S161. doi: 10.4103/aian.AIAN_557_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Maso I, Pinto EB, Monteiro M, et al. A Simple Hospital Mobility Scale for Acute Ischemic Stroke Patients Predicts Long-term Functional Outcome. Neurorehabil Neural Repair. 2019;33(08):614–622. doi: 10.1177/1545968319856894. [DOI] [PubMed] [Google Scholar]
  • 25.Cincura C, Pontes OM, Neto, Neville IS, et al. Validation of the National Institutes of Health Stroke Scale, modified Rankin Scale and Barthel Index in Brazil: the role of cultural adaptation and structured interviewing. Cerebrovasc Dis. 2009;27(02):119–122. doi: 10.1159/000177918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Fama ME, Turkeltaub PE. Treatment of poststrokeaphasia: current practice and new directions. Semin Neurol. 2014;34(05):504–513. doi: 10.1055/s-0034-1396004. [DOI] [PubMed] [Google Scholar]
  • 27.de Carvalho IA, Mansur LL. Validation of ASHA FACS-functional assessment of communication skills for Alzheimer disease population. Alzheimer Dis Assoc Disord. 2008;22(04):375–381. doi: 10.1097/wad.0b013e31818809b2. [DOI] [PubMed] [Google Scholar]
  • 28.de Carvalho IAM, Bahia VS, Mansur LL. Functional communication ability in frontotemporal lobar degeneration and Alzheimer’s disease. Dement Neuropsychol. 2008;2(01):31–36. doi: 10.1590/S1980-57642009DN20100007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Paul D. In: Encyclopedia of Clinical Neuropsychology. Kreutzer J., DeLuca J., Caplan B., editors. Springer, Cham; 2017. American Speech-Language-Hearing Association Functional Assessment of Communication Skills for Adults. [Google Scholar]
  • 30.Gialanella B, Prometti P, Vanoglio F, Comini L, Santoro R. Aphasia and activities of daily living in stroke patients. Eur J Phys Rehabil Med. 2016;52(06):782–790. [PubMed] [Google Scholar]
  • 31.Kim G, Min D, Lee EO, Kang EK. Impact of Co-occurring Dysarthria and Aphasia on Functional Recovery in Post-stroke Patients. Ann Rehabil Med. 2016;40(06):1010–1017. doi: 10.5535/arm.2016.40.6.1010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lee H, Lee Y, Choi H, Pyun SB. Community Integration and Quality of Life in aphasia after stroke. Yonsei Med J. 2015;56(06):1694–1702. doi: 10.3349/ymj.2015.56.6.1694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Ginex V, Veronelli L, Vanacore N, Lacorte E, Monti A, Corbo M. Motor recovery in post-stroke patients with aphasia: the role of specific linguistic abilities. Top Stroke Rehabil. 2017;24(06):428–434. doi: 10.1080/10749357.2017.1305654. [DOI] [PubMed] [Google Scholar]
  • 34.Voos MC, Ribeiro do Valle LE. Comparative study on the relationship between stroke hemisphere and functional evolution in right-handed individuals. Rev Bras Fisioter. 2008;12(02):113–120. [Google Scholar]
  • 35.Sabaté M, González B, Rodríguez M. Brain lateralization of motor imagery: motor planning asymmetry as a cause of movement lateralization. Neuropsychologia. 2004;42(08):1041–1049. doi: 10.1016/j.neuropsychologia.2003.12.015. [DOI] [PubMed] [Google Scholar]
  • 36.Baldo JV, Paulraj SR, Curran BC, Dronkers NF. Impaired reasoning and problem-solving in individuals with language impairment due to aphasia or language delay. Front Psychol. 2015 Oct 26;6:1523–1523. doi: 10.3389/fpsyg.2015.01523. PMID: 26578991; PMCID: PMC4620683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kim KA, Lee JS, Chang WH, et al. Changes in Language Function and Recovery-Related Prognostic Factors in First-Ever Left Hemispheric Ischemic Stroke. Ann Rehabil Med. 2019;43(06):625–634. doi: 10.5535/arm.2019.43.6.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.González-Fernández M, Davis C, Molitoris JJ, Newhart M, Leigh R, Hillis AE. Formal education, socioeconomic status, and the severity of aphasia after stroke. Arch Phys Med Rehabil. 2011;92(11):1809–1813. doi: 10.1016/j.apmr.2011.05.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Ortiz KZ, Costa FP. M1-Alpha test in normal subjects with low educational level: a pilot study. J Soc Bras Fonoaudiol. 2011;23(03):220–226. doi: 10.1590/s2179-64912011000300007. [DOI] [PubMed] [Google Scholar]

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