Background: Knowledge of stroke is essential to empower people to reduce their risk of these events. However, valid tools are required for accurate and reliable measurement of stroke knowledge. We aimed to systematically review contemporary stroke knowledge assessment tools and appraise their content validity, feasibility, and measurement properties. Methods: The protocol was registered in PROSPERO (CRD42023403566). Electronic databases (MEDLINE, PsycInfo, CINAHL, Embase, Scopus, Web of Science) were searched to identify published articles (1 January 2015–1 March 2023), in which stroke knowledge was assessed using a validated tool. Two reviewers independently screened titles and abstracts prior to undertaking full-text review. COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methods guided the appraisal of content validity (relevance, comprehensiveness, comprehensibility), feasibility, and measurement properties. Results: After removing duplicates, the titles and abstracts of 718 articles were screened; 323 reviewed in full; with 42 included (N = 23 unique stroke knowledge tools). For content validity, all tools were relevant, two were comprehensive, and seven were comprehensible. Validation metrics were reported for internal consistency (n = 20 tools), construct validity (n = 17 tools), cross-cultural validity (n = 15 tools), responsiveness (n = 9 tools), reliability (n = 7 tools), structural validity (n = 3 tools), and measurement error (n = 1 tool). The Stroke Knowledge Test met all content validity criteria, with validation data for six measurement properties (n = 3 rated “Sufficient”). Conclusion: Assessment of stroke knowledge is not standardised and many tools lacked validated content or measurement properties. The Stroke Knowledge Test was the most comprehensive but requires updating and further validation for endorsement as a gold standard.

Globally, more than 12.2 million people experienced a stroke in 2019, with approximately half being fatal [1]. Stroke is reported to result in the loss of >143 million years of healthy life due to death and disability annually [1]. Despite this burden, stroke remains highly preventable, with ten potentially modifiable risk factors accounting for >90% of the population attributable risk of stroke [2].

Knowledge of stroke, including how to manage risk factors effectively and recognise warning signs, is important to prevent stroke and seek prompt medical care when signs and symptoms first occur [3‒6]. Several clinical trials and health promotion programs are currently underway to improve the knowledge of stroke in the community to support primary prevention [7, 8]. Furthermore, the World Stroke Organization has established advocacy priorities focused on “improving knowledge in the population of individual risk of stroke occurrence and symptoms of stroke and on the benefits of timely admission to hospital” [9]. Given the importance of improving stroke knowledge in the population, valid and reliable tools are urgently needed to support accurate measurement and evaluation of interventions aimed at enhancing stroke knowledge.

In a previous systematic review of stroke knowledge tools used from 2000–2014, Hou et al. [10] reported a lack of quality or gold standard tools for assessing knowledge of stroke and its risk factors. Recent advances in systematic review methodology for patient-reported outcome measures provide scope for more rigorous evaluation of measurement properties [11]. Therefore, an updated review was needed to identify contemporaneous, valid, and reliable tools for measuring stroke knowledge (termed stroke knowledge tools hereafter). In this study, we aimed to systematically search for contemporary stroke knowledge tools, and appraise their content, feasibility, and measurement properties.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement [12] (checklist provided in online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000535292). The protocol was submitted to PROSPERO (CRD42023403566) on 28 February 2023, prior to searching the databases. Notification of protocol registration was received on 10 March 2023 (at the stage of screening articles).

Eligibility Criteria

Types of Studies

We included all types of studies in which stroke knowledge was assessed using a tool, published in any language. Conference abstracts and review articles were excluded.

Types of Participants

We included all populations, with or without pre-existing cardiovascular disease.

Types of Exposure

We included studies conducted where a stroke knowledge tool was developed or adapted for a group of participants, for the purpose of measuring knowledge about primary or secondary prevention of stroke or transient ischaemic attack. Given our focus on identifying a gold standard stroke knowledge tool, tools without a validation metric reported were also excluded.

Types of Outcome Measures

We applied COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology to evaluate specific measurement properties of the stroke knowledge tools [11]. COSMIN provides a methodological guideline to conduct systematic reviews of patient-reported outcome measures in a transparent and standardised way. The following measurement properties are appraised as part of COSMIN:

  • Content validity (i.e., the degree to which the content of the tool is an adequate reflection of the construct to be measured)

  • Structural validity (i.e., the degree to which the scores of the tool are an adequate reflection of the dimensionality of the construct to be measured)

  • Internal consistency (i.e., the degree of the interrelatedness among the items)

  • Reliability (i.e., the proportion of the total variance in the measurements which is due to “true” differences between patients)

  • Measurement error (i.e., the systematic and random error of a respondent’s score that is not attributed to true changes in the construct to be measured)

  • Hypotheses testing for construct validity (i.e., the degree to which the scores of tool items are consistent with hypotheses)

  • Cross-cultural validity (i.e., the degree to which the performance of the items on a translated or culturally adapted tool are an adequate reflection of the performance of the items of the original version of the tool)

  • Responsiveness (i.e., the ability of a tool to detect change over time in the construct to be measured)

  • Criterion validity (i.e., the degree to which the scores are an adequate reflection of a “gold standard”). This property was not examined as part of this review due to the lack of a gold standard stroke knowledge tool.

We were also interested in outcomes related to the feasibility of tools (e.g., completion time, accessibility, length, ease of administration).

Search Methods

Searches

We searched six electronic databases (Ovid MEDLINE, PsycInfo, CINAHL, Ovid Embase, Scopus, Web of Science) from 1 January 2015 to 1 March 2023, with no language restrictions. The earliest date of 1 January 2015 was selected to ensure the retrieved stroke knowledge tools were contemporary and published after the previous review by Hou et al. [10]. With assistance from a Monash University librarian, the search strategy from Hou et al. [10] was refined by using a common proximity search operator (three words between key terms), limiting keyword searches to title and abstracts, and employing subject headings in applicable databases (online suppl. Tables 2–7).

After full-text review, two authors (A.S., K.V.) undertook backward citation searching to identify any additional articles from the reference lists of included articles. Forward citation searching was also performed by one author (L.L.D.) using Google Scholar.

Selection Process

Selection of Studies

Using Covidence, two authors (L.L.D., C.B.) screened the titles and abstracts independently, and then the full-texts independently. Discrepancies at each step were resolved through discussion with a third author (R.F.P.).

When the stroke knowledge tool was not available (e.g., not published in the article, supplementary material, a related article, or internet source), the authors were contacted at least twice to request a copy of the tool. The article was excluded if the stroke knowledge tool could not be obtained. Google Translate (www.translate.google.com) and a bilingual expert were used to translate studies and tools unavailable in English.

Modifications to the Protocol

We excluded studies conducted exclusively in health professionals to align with our specific focus on patient-reported outcome measures, as indicated in our protocol title. We also excluded video-based stroke knowledge tools as these were deemed to be beyond the scope of this review.

Data Extraction, Analysis, and Synthesis

Data Extraction

Once relevant stroke knowledge tools were identified, the articles using these tools were grouped together. One author (L.L.D.) undertook data extraction using a pre-specified REDCap form hosted by Monash University [13, 14]. A second author (C.B.) reviewed the extracted data, and any disagreements were resolved by a consensus discussion with a third author (R.F.P. or M.F.K.). Data extracted comprised the study characteristics (e.g., country, year, sample size, age, sex of participants), features of the stroke knowledge tools (e.g., response options, language), feasibility (e.g., number of items, time to complete, accessibility), content (e.g., concepts of stroke knowledge covered), and measurement properties. Up to two attempts were made to contact authors of included publications to obtain clarification or additional data, when required.

COSMIN Methodology

Data on each COSMIN measurement property were extracted by one author (L.L.D.) and rated based on an adapted version of the “COSMIN Criteria for Good Measurement Properties” (see online suppl. Table 8) [15]. The assignment of each rating (either sufficient [+], insufficient [−], inconsistent [±], or indeterminate [?]), was reviewed by a second author (C.B.). COSMIN methodology also guided the evaluation of content validity, in terms of relevance, comprehensiveness, and comprehensibility. For relevance, our inclusion criteria ensured that all tools retrieved were relevant for assessing stroke knowledge. For comprehensiveness, four authors (L.L.D., C.B., M.F.K., R.F.P.) reached consensus on the major concepts essential for assessing stroke knowledge. These included the definition of stroke, stroke statistics, stroke risk factors, risk factor management, warning signs and symptoms, medical action/response, treatments for stroke, and impacts of stroke. Two or more authors (L.L.D., C.B., M.F.K., R.F.P.) independently reviewed each tool for the presence of these concepts, considering the tool to be comprehensive if ≥85% of concepts were covered [16]. A checklist was developed based on the COSMIN checklist (see online suppl. Table 9) and used by four authors (L.L.D., C.B., M.F.K., R.F.P.) to assess comprehensibility across five criteria [15]. These comprised: (1) instruction wording; (2) item wording; (3) response option wording; (4) grammar and spelling; and (5) ease of reading. Ease of reading was assessed using a computer-generated Flesch Kincaid Reading Ease score (www.webpagefx.com/tools/read-able), with scores ≥60 considered to be easily understood by people aged 15–16 years. The tool was considered comprehensible if four of five comprehensibility criteria were met.

Data Synthesis

The main findings were presented in a table format, with aggregated data provided at the level of the stroke knowledge tool. Data were summarised using descriptive statistics, and results were narratively synthesised.

Search Results

The search strategy yielded 718 unique articles (Fig. 1). Titles and abstracts of these articles were screened independently by two authors (90% agreement; κ = 0.79). Among the 718 articles, 323 were reviewed in full by two authors, of which 289 articles did not meet the inclusion criteria and were excluded (98% agreement; k = 0.90). The majority of articles excluded at the full-text stage lacked a COSMIN validation metric (n = 182). A further 29 articles (n = 22 unique tools) were excluded following ≥2 contact attempts with the corresponding author(s) to obtain a copy of the stroke knowledge tool. After including eight additional articles through forward and backward citation searching, 42 articles were included, covering 23 unique stroke knowledge tools.

Fig. 1.

Summary of literature search strategy and included articles.

Fig. 1.

Summary of literature search strategy and included articles.

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Article Characteristics

Of the 42 included articles, the largest proportion were from the USA (24%), followed by Thailand (17%), Malaysia (10%), Ethiopia (7%), and other countries (43%). Approximately half (45.2%) of the studies were undertaken in a community setting, followed by hospital inpatient or outpatient settings (38.1%), schools or universities (11.9%), primary care practices (2.4%), or online (2.4%). The median sample size was 229 participants (range: 30–3,070). In studies where data were available on sex (n = 40) and mean age (n = 37) of participants, 55% were female and the mean age was 54.8 years. Further details of these studies are provided in online supplementary Table 10.

Tool Characteristics

Most tools were used in a target population of community-dwelling adults, with or without pre-existing vascular disease (Table 1). Three tools were used specifically in a cohort of children [17] and/or their parents [18, 19]. Of the 23 included tools, 18 were developed or validated in English language, and a further four had English translations available from the study article or authors. One tool [20] was translated from Thai to English by a bilingual expert for this review. The Stroke Action Test [21], Stroke Knowledge Test [22], and Stroke Preparedness Vignettes [23] were the most commonly used tools in the published literature, post-2015.

Table 1.

Retrieved validated stroke knowledge tools and availability of translations

Name of tool*Target populationData collection modeLanguageCountryStudies using this tool
Ahmed et al. [24] 2019 General population In-person interview Malay, English Malaysia Ahmed et al. [24] 2019 
Ananchaisarp and Sa-a [20] 2022 People with diabetes and/or hypertension In-person interview OR Self-completed questionnaire Thai Thailand Ananchaisarp and Sa-a [20] 2022 
Das et al. [25] 2016 Community-dwelling residents in India In-person interview Bengali, Hindi India Das et al. [25] 2016 
FAST 112 Heroes Stroke Preparedness Questionnaire [18Parents of children participating in a health education program Self-completed questionnaire sent home with child Greek Greece Tsakpounidou and Proios [18] 2021 
Kaddumukasa et al. [26] 2015 General population and high-risk population In-person interview Not reported Sudan Kaddumukasa et al. [26] 2015 
Mohammed et al. [27] 2020 
Knowledge of Stroke Risk survey [28African American/Black, aged 20–40 years, with risk factor(s) for stroke In-person interview English USA Aycock et al. [28] 2015 
Aycock et al. [29] 2017 
Deepthi et al. [30] 2022 Relatives of patients attending a neurology outpatient clinic In-person interview OR Self-completed questionnaire Malayalam India Deepthi et al. [30] 2022 
Nigat et al. [31] 2021 Hypertensive adults In-person interview English, Amharic Ethiopia Nigat et al. [31] 2021 
Oh and Yang [32] 2021 Patients with atrial fibrillation Self-completed questionnaire Korean, English Korea Oh and Yang [32] 2021 
Salam et al. [17] 2022 Children, aged 11–18 years Self-completed questionnaire Arabic, English Qatar Salam et al. [17] 2022 
Singhard [33] 2011 Adult patients with recurrent stroke/TIA In-person interview Thai Thailand Saengsuwan et al. [34] 2017 
Saengsuwan and Suangpho [35] 2019 
Stroke Action Test [21General population Self-completed questionnaire English [21USA O’Connell et al. [36] 2022 
Martinez et al. [37] 2016 
Omelchenko et al. [38] 2018 
Williams et al. [39] 2016 
Williams et al. [40] 2019 
Saadi et al. [41] 2020 
Chinese [42China Ha et al. [42] 2016 
Italian [43Italy Denti et al. [43] 2015 
Caminti et al. [44] 2017 
Stroke Action Test (Adapted) [19Children and their parents In-person interview English USA Williams et al. [19] 2018 
Telephone interview 
Self-completed questionnaire 
Stroke Knowledge Assessment Tool [45General population Self-completed online survey English Malta Grech and Grech [45] 2021 
Stroke Knowledge Questionnaire [46Community-dwelling survivors of stroke or their caregivers In-person interview English Australia Hoffmann et al. [47] 2015 
Stroke Knowledge Test [22General population Self-completed questionnaire English [22Australia 
Patients with stroke Self-completed questionnaire Malay [48Malaysia Appalasamy et al. [49] 2020 
Sowtali et al. [48] 2016 
Sowtali et al. [50] 2017 
General population Self-completed online survey Arabic [51Lebanon Saade et al. [51] 2022 
Adults with recurrent stroke or TIA In-person interview Bahasa Indonesian [52Indonesia Widjaja et al. [52] 2021 
Patients, relatives, or visitors at an emergency department In-person interview Tunisian [53Republic of Tunisia Chakroun-Walha et al. [53] 2021 
Stroke Awareness Questionnaire [54Older community-dwelling adults In-person interview Thai Thailand Pothiban et al. [54] 2018 
Pothiban and Srirat [55] 2019 
Stroke Knowledge and Sources of Knowledge Questionnaire [54Older community-dwelling adults In-person interview Thai Thailand Pothiban et al. [54] 2018 
Pothiban and Srirat [55] 2019 
Stroke Literacy Scale [56Adults at high risk of stroke and their family members Self-completed questionnaire Thai Thailand Waelveerakup et al. [56] 2019 
Stroke Preparedness Vignettes [23African American church attendees Online survey English USA Beal [57] 2017 
General population Self-completed questionnaire English United Kingdom Dombrowski et al. [58] 2015a 
Dombrowski 2015b [59
Wilhelm et al. [60] 2020 
Vietnamese Americans Self-completed questionnaire Vietnamese [61USA Phan et al. [61] 2018 
Sungbun et al. [62] 2022 Community-dwelling ethnic groups in Thailand Telephone interview Thai Thailand Sungbun et al. [62] 2022 
Tibebu et al. [63] 2021 Hypertensive adults In-person interview Amharic, English Ethiopia Tibebu et al. [63] 2021 
Workina et al. [64] 2021 Patients with heart disease In-person interview English, Amharic, Afan Oromo Ethiopia Workina et al. [64] 2021 
Name of tool*Target populationData collection modeLanguageCountryStudies using this tool
Ahmed et al. [24] 2019 General population In-person interview Malay, English Malaysia Ahmed et al. [24] 2019 
Ananchaisarp and Sa-a [20] 2022 People with diabetes and/or hypertension In-person interview OR Self-completed questionnaire Thai Thailand Ananchaisarp and Sa-a [20] 2022 
Das et al. [25] 2016 Community-dwelling residents in India In-person interview Bengali, Hindi India Das et al. [25] 2016 
FAST 112 Heroes Stroke Preparedness Questionnaire [18Parents of children participating in a health education program Self-completed questionnaire sent home with child Greek Greece Tsakpounidou and Proios [18] 2021 
Kaddumukasa et al. [26] 2015 General population and high-risk population In-person interview Not reported Sudan Kaddumukasa et al. [26] 2015 
Mohammed et al. [27] 2020 
Knowledge of Stroke Risk survey [28African American/Black, aged 20–40 years, with risk factor(s) for stroke In-person interview English USA Aycock et al. [28] 2015 
Aycock et al. [29] 2017 
Deepthi et al. [30] 2022 Relatives of patients attending a neurology outpatient clinic In-person interview OR Self-completed questionnaire Malayalam India Deepthi et al. [30] 2022 
Nigat et al. [31] 2021 Hypertensive adults In-person interview English, Amharic Ethiopia Nigat et al. [31] 2021 
Oh and Yang [32] 2021 Patients with atrial fibrillation Self-completed questionnaire Korean, English Korea Oh and Yang [32] 2021 
Salam et al. [17] 2022 Children, aged 11–18 years Self-completed questionnaire Arabic, English Qatar Salam et al. [17] 2022 
Singhard [33] 2011 Adult patients with recurrent stroke/TIA In-person interview Thai Thailand Saengsuwan et al. [34] 2017 
Saengsuwan and Suangpho [35] 2019 
Stroke Action Test [21General population Self-completed questionnaire English [21USA O’Connell et al. [36] 2022 
Martinez et al. [37] 2016 
Omelchenko et al. [38] 2018 
Williams et al. [39] 2016 
Williams et al. [40] 2019 
Saadi et al. [41] 2020 
Chinese [42China Ha et al. [42] 2016 
Italian [43Italy Denti et al. [43] 2015 
Caminti et al. [44] 2017 
Stroke Action Test (Adapted) [19Children and their parents In-person interview English USA Williams et al. [19] 2018 
Telephone interview 
Self-completed questionnaire 
Stroke Knowledge Assessment Tool [45General population Self-completed online survey English Malta Grech and Grech [45] 2021 
Stroke Knowledge Questionnaire [46Community-dwelling survivors of stroke or their caregivers In-person interview English Australia Hoffmann et al. [47] 2015 
Stroke Knowledge Test [22General population Self-completed questionnaire English [22Australia 
Patients with stroke Self-completed questionnaire Malay [48Malaysia Appalasamy et al. [49] 2020 
Sowtali et al. [48] 2016 
Sowtali et al. [50] 2017 
General population Self-completed online survey Arabic [51Lebanon Saade et al. [51] 2022 
Adults with recurrent stroke or TIA In-person interview Bahasa Indonesian [52Indonesia Widjaja et al. [52] 2021 
Patients, relatives, or visitors at an emergency department In-person interview Tunisian [53Republic of Tunisia Chakroun-Walha et al. [53] 2021 
Stroke Awareness Questionnaire [54Older community-dwelling adults In-person interview Thai Thailand Pothiban et al. [54] 2018 
Pothiban and Srirat [55] 2019 
Stroke Knowledge and Sources of Knowledge Questionnaire [54Older community-dwelling adults In-person interview Thai Thailand Pothiban et al. [54] 2018 
Pothiban and Srirat [55] 2019 
Stroke Literacy Scale [56Adults at high risk of stroke and their family members Self-completed questionnaire Thai Thailand Waelveerakup et al. [56] 2019 
Stroke Preparedness Vignettes [23African American church attendees Online survey English USA Beal [57] 2017 
General population Self-completed questionnaire English United Kingdom Dombrowski et al. [58] 2015a 
Dombrowski 2015b [59
Wilhelm et al. [60] 2020 
Vietnamese Americans Self-completed questionnaire Vietnamese [61USA Phan et al. [61] 2018 
Sungbun et al. [62] 2022 Community-dwelling ethnic groups in Thailand Telephone interview Thai Thailand Sungbun et al. [62] 2022 
Tibebu et al. [63] 2021 Hypertensive adults In-person interview Amharic, English Ethiopia Tibebu et al. [63] 2021 
Workina et al. [64] 2021 Patients with heart disease In-person interview English, Amharic, Afan Oromo Ethiopia Workina et al. [64] 2021 

TIA, transient ischaemic attack.

*First author(s) of the original article listed for tools without a name.

In which the tool was initially validated.

Published between 1 January 2015 and 1 March 2023 and reported data from tool in results.

Feasibility of Tools

Only 30.4% (7/23) of tools had a version of the tool publicly available, while the remainder were available on request from the authors or original creator [18, 19, 22, 25, 26, 45, 64]. All tools were accessed at no cost. The tools contained a median of 17 items, with 19 tools (83%) comprising multiple choice responses only (Table 2). The average completion time was reported in five articles [18, 21, 32, 41, 42, 54]. Completion of the Fast 112 Heroes Stroke Preparedness Questionnaire [18] and the Stroke Action Test [21, 41, 42] were reported to take approximately 5–10 min. However, completion of the Stroke Knowledge and Sources of Knowledge Questionnaire and Stroke Awareness Questionnaire were each reported to take >30 min [54]. Only 11 (47.8%) tools had evidence from a published article that the tool could be self-completed by participants.

Table 2.

Appraisal of content validity of each stroke knowledge tool

Total number of itemsResponse optionsRelevant?ComprehensivenessComprehensibility
definition of strokestroke statisticsstroke risk factor(s)risk factor managementwarning sign(s)/symptom(s)medical action/responsetreatment(s) for strokeimpact(s) of strokecomprehensive?*instruction wordingitem wordingresponse option wordinggrammar and spellingeasy to read†comprehensible?
Ahmed et al. [24] 2019 25 MC +   ✓  ✓ ✓    Y (81.5)  
Ananchaisarp and Sa-a [20] 2022 23 MC, SA +   ✓ ✓ ✓ ✓    Y (72.3) + 
Das et al. [25] 2016 16 MC (Y/N/U) +   ✓ ✓ ✓ ✓ ✓ ✓  Y (64.7)  
FAST 112 Heroes Stroke Preparedness Questionnaire [18MC, SA + ✓    ✓ ✓    Y (90.4) + 
Kaddumukasa et al. [26] 2015 11 MC (inc. O) + ✓  ✓  ✓ ✓ ✓   Y (71.8)  
Knowledge of Stroke Risk Survey [2815 MC (Y/N/U) +   ✓       N (47.9) + 
Deepthi et al. [30] 2022 MC + ✓  ✓  ✓ ✓    Y (68.5)  
Nigat et al. [31] 2021 31 MC (Y/N) +   ✓  ✓     Y (83.7)  
Oh and Yang [32] 2021 17 MC (Y/N/U) +     ✓     N (58.9)  
Salam et al. [17] 2022 36 MC + ✓  ✓  ✓ ✓ ✓   Y (83.5) + 
Singhard et al. [33] 2011 MC + ✓  ✓ ✓ ✓ ✓ ✓   Y (71.8)  
Stroke Action Test [2128 MC (4 options) +     ✓ ✓    Y (82.8)  
Stroke Action Test (Adapted) [1912 MC, SA +     ✓ ✓    Y (95.6) + 
Stroke Knowledge Assessment Tool [4534 MC (5-point scale) + ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ + Y (60.9)  
Stroke Knowledge Questionnaire [4625 MC (Y/N/U) + ✓  ✓ ✓ ✓ ✓  ✓  Y (93.4)  
Stroke Knowledge Test [2220 MC (5 options, including U) + ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ + Y (86.2) + 
Stroke Awareness Questionnaire [5414 MC (5-point scale) +        ✓  N (36.8)  
Stroke Knowledge and Sources of Knowledge Questionnaire [5422 MC, SA + ✓  ✓ ✓ ✓ ✓ ✓   N (54.7)  
Stroke Literacy Scale [5646 MC (Y/N)MC (Y/N/U) +   ✓ ✓ ✓ ✓ ✓   Y (61.4)  
Stroke Preparedness Vignettes [2316 MC (Y/N/U), MC (4 options) +     ✓ ✓    Y (74.7) + 
Sungbun 2022 [6210 MC + ✓  ✓  ✓ ✓ ✓   Y (80.8)  
Tibebu et al. [63]2021 11 MC (Y/N) +    ✓      Y (76.2)  
Workina et al. [64]2021 11 MC +   ✓  ✓ ✓    Y (87.1)  
Total number of itemsResponse optionsRelevant?ComprehensivenessComprehensibility
definition of strokestroke statisticsstroke risk factor(s)risk factor managementwarning sign(s)/symptom(s)medical action/responsetreatment(s) for strokeimpact(s) of strokecomprehensive?*instruction wordingitem wordingresponse option wordinggrammar and spellingeasy to read†comprehensible?
Ahmed et al. [24] 2019 25 MC +   ✓  ✓ ✓    Y (81.5)  
Ananchaisarp and Sa-a [20] 2022 23 MC, SA +   ✓ ✓ ✓ ✓    Y (72.3) + 
Das et al. [25] 2016 16 MC (Y/N/U) +   ✓ ✓ ✓ ✓ ✓ ✓  Y (64.7)  
FAST 112 Heroes Stroke Preparedness Questionnaire [18MC, SA + ✓    ✓ ✓    Y (90.4) + 
Kaddumukasa et al. [26] 2015 11 MC (inc. O) + ✓  ✓  ✓ ✓ ✓   Y (71.8)  
Knowledge of Stroke Risk Survey [2815 MC (Y/N/U) +   ✓       N (47.9) + 
Deepthi et al. [30] 2022 MC + ✓  ✓  ✓ ✓    Y (68.5)  
Nigat et al. [31] 2021 31 MC (Y/N) +   ✓  ✓     Y (83.7)  
Oh and Yang [32] 2021 17 MC (Y/N/U) +     ✓     N (58.9)  
Salam et al. [17] 2022 36 MC + ✓  ✓  ✓ ✓ ✓   Y (83.5) + 
Singhard et al. [33] 2011 MC + ✓  ✓ ✓ ✓ ✓ ✓   Y (71.8)  
Stroke Action Test [2128 MC (4 options) +     ✓ ✓    Y (82.8)  
Stroke Action Test (Adapted) [1912 MC, SA +     ✓ ✓    Y (95.6) + 
Stroke Knowledge Assessment Tool [4534 MC (5-point scale) + ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ + Y (60.9)  
Stroke Knowledge Questionnaire [4625 MC (Y/N/U) + ✓  ✓ ✓ ✓ ✓  ✓  Y (93.4)  
Stroke Knowledge Test [2220 MC (5 options, including U) + ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ + Y (86.2) + 
Stroke Awareness Questionnaire [5414 MC (5-point scale) +        ✓  N (36.8)  
Stroke Knowledge and Sources of Knowledge Questionnaire [5422 MC, SA + ✓  ✓ ✓ ✓ ✓ ✓   N (54.7)  
Stroke Literacy Scale [5646 MC (Y/N)MC (Y/N/U) +   ✓ ✓ ✓ ✓ ✓   Y (61.4)  
Stroke Preparedness Vignettes [2316 MC (Y/N/U), MC (4 options) +     ✓ ✓    Y (74.7) + 
Sungbun 2022 [6210 MC + ✓  ✓  ✓ ✓ ✓   Y (80.8)  
Tibebu et al. [63]2021 11 MC (Y/N) +    ✓      Y (76.2)  
Workina et al. [64]2021 11 MC +   ✓  ✓ ✓    Y (87.1)  

MC, multiple choice; N, no; O, open-ended other option (e.g., please specify); Y, yes; SA, short answer; U, unknown, unsure or uncertain; ✓, concept covered in tool; +, tool meets criteria.

*Considered to be comprehensive if ≥85% of concepts (i.e., 7/8) are covered in the tool [16].

Based on the Flesch Kincaid Reading Ease Score, with values ≥60 required to ensure a reading level below Grade 10 (i.e., easily understood by people aged 15–16 years).

Considered to be comprehensible if ≥80% (i.e., 4/5) of the comprehensibility criteria are met.

Content Validity of Tools

Thirteen tools (56.5%) had an associated study adequately describing how the content was developed. Most commonly, this involved the tool being reviewed for content and face validity by clinicians, researchers, patients, and/or members of the general public. Other quantitative measures of content validity are outlined in online supplementary Table 11. The Stroke Knowledge Assessment Tool [45] and the Stroke Preparedness Vignettes [23] were also developed using qualitative methods, involving interviews or focus groups with members of the target population.

Results of our appraisal of the content validity, based on the COSMIN methodology, are provided in Table 2. The Stroke Knowledge Assessment Tool [45] and the Stroke Knowledge Test [22] fulfilled our comprehensiveness criteria and covered ≥85% (7/8) of the concepts deemed important for assessing stroke knowledge. In terms of comprehensibility, seven tools fulfilled at least 4/5 criteria related to instruction wording, item wording, response option wording, grammar and spelling, and ease of reading [17‒20, 22, 23, 28]. The Knowledge of Stroke Risk survey [28] and Salam 2022 tool [17] fulfilled all five comprehensibility criteria.

Measurement Properties of Tools

Structural Validity

Evidence on the structural validity was unavailable for most (20/23) tools (Table 3). Exploratory factor analysis was undertaken in the Chinese Stroke Action Test [42], with four factors reported to explain 48.4% of the total variance of the instrument. Similarly, in the Stroke Action Test (Adapted) [19], the explained common variance was 25.5–73.0% in children, and >45% in parents. Item difficulty and discrimination indices were reported for the Stroke Knowledge Test [22] which suggests an item analysis was undertaken. No tool explicitly fulfilled the COSMIN criteria for sufficient structural validity [15].

Table 3.

COSMIN assessment of the measurement properties of stroke knowledge tools

ToolStructural validityInternal consistencyReliabilityMeasurement errorHypotheses testing for construct validityCross-cultural validityResponsiveness
Ahmed et al. [24] 2019  + Cronbach’s α: 0.83 [24+ Test-retest reliability after 2 weeks (n = 10), Spearman’s correlation: 0.954 [24 + Prior stroke associated with greater scores (p = 0.034) [24+ No significant differences by education, income, or race [24 
Ananchaisarp and Sa-a [20] 2022   + Test-retest reliability: 0.9 (prevention), 0.7 (warning symptoms) [20 ? Being unaware of stroke risk associated with greater scores [20- Greater income associated with higher scores (p = 0.04) [20 
Das et al. [25] 2016   ± Similar responses (ICC: 0.6–0.9 for each item) in sample (n = 50) assessed separately by two reviewers [25 ± Stroke affected families had greater awareness of stroke (p < 0.001), but poorer knowledge of some risk factors [25  
FAST 112 Heroes Stroke Preparedness Questionnaire [18 ± Cronbach’s α: 0.67–0.73 [18  + Knowing someone with stroke associated with increased scores (p < 0.001) [18- Master or PhD education (vs. other) associated with greater scores (p = 0.018) [18+ Median scores improved after intervention (p < 0.001) [18
Kaddumukasa et al. [26] 2015  + Cronbach’s α: 0.7 [27  ± Mean scores were similar between high and low risk participants (p = 0.72) [27± No differences in scores by sex (p = 0.89). Scores greater for individuals with secondary or university education (p < 0.001) [27 
Knowledge of Stroke Risk survey [28 ± Cronbach’s α: 0.70 [28], 0.45–0.80 [29  ± Family history of stroke had no effect on scores but was recognised as a risk factor (p < 0.05) [28 + Mean scores increased by 13% in intervention (vs. 5% in control) [29
Deepthi et al. [30] 2022  + Cronbach’s α: 0.7 [30   - Scores not comparable with other studies from India [30 
Nigat et al. [31] 2021  + Cronbach’s α: 0.71 [31  ? Long-standing hypertension (>5 years vs. ≤5 years) associated with increased scores [31- Scores greater for educated individuals able to read and write and those from urban (vs. rural) locations [31 
Oh and Yang [32] 2021  + KR-20: 0.74–0.91 [32  - Scores similar between patients with and without pre-existing vascular disease [32± Scores differed by age (p = 0.12) and education (p < 0.001), but not income level (p = 0.78) [32 
Salam et al. [17] 2022  + Cronbach’s α: 0.92 [17    + Mean scores increased post-intervention (β: 1.1–1.4; p < 0.001) and were sustained at 2 months (β: 0.8–0.7; p < 0.001) [17
Singhard et al. [33] 2011  + KR-20: 0.95 (personal communication)   ? Being aware of one’s own stroke risk did not influence scores [35+ No difference in scores based on age, gender, living situation, and education level [34 
Stroke Action Test (STAT) [21? EFA KMO: 0.87 (Bartlett’s p < 0.001) Four factors explained 48.35% of the total instrument variance [42+ Cronbach’s α: 0.83 [21], 0.85 [43], 0.89 [42], 0.91 [36+ Test-rest reliability: 0.86 [42 + Medical students had higher scores than general public (p = 0.011) [21]. Experience of stroke associated with greater scores among general public (p < 0.001) [21? Scores were lower in Hispanic (vs. non-Hispanic White) people. Scores differed by ethnicity [51]. Scores increased in people with 0–11 years of education (vs. higher) [40+ Scores increased following interventions [38, 39
Stroke Action Test (Adapted STAT) [19? ECV: 25.5–73% (Children) and >45% (Parents) [19± Cronbach’s α Children (0.43–0.87) [18] Parents (0.73–0.75) [19 + Increase in scores in parents (50–80% correct) reported to be a clinically important difference [19+ Scores increased following educational intervention versus control [19? No difference in scores based on health literacy [19+ Absolute improvement of 55% in children receiving intervention versus 0% in control group, immediately after program (p < 0.001) [19
Stroke Knowledge Assessment Tool [45 + Cronbach’s α: 0.78–0.83 [45+ Test-retest reliability after 30 days (n = 118), ICC: 0.79 [45    
Stroke Knowledge Questionnaire [46  - Test-retest after 4 weeks (n = 40), ICC: 0.66 [46   - Scores similar after intervention (potentially underpowered) [47
Stroke Knowledge Test [22? Item difficulty and discrimination indices examined. Discrimination ≥0.375 ensured for each item [22± Cronbach’s α: 0.65–0.69 [60], 0.65–0.70 [22], 0.78 [53], 0.88 [51], KR-20: 0.58 [48+ Immediate test-retest (n = 25), Pearson’s correlation: 0.82 [22 + Scores greater in those with a family history of stroke [22- Scores differed by age, education, insurance coverage, location, smoking status, alcohol intake, and comorbidities [51+ Scores improved following delivery of stroke information (p < 0.001) but not non-stroke information [22
Stroke Awareness Questionnaire [54 + Cronbach’s α: 0.84–0.86 [54  - Scores similar between people with and without stroke risk [55  
Stroke Knowledge and Sources of Knowledge Questionnaire [54 + KR-20: 0.83–0.84 [29]   - Scores similar between people with and without stroke risk [55  
Stroke Literacy Scale [56 + KR-21:Family (0.87–0.89) [56]; People at high risk of stroke (0.90) [56  - Scores not associated with stroke pre-hospital delay behaviour intention [56  
Stroke Preparedness Vignettes [23 + Cronbach’s α: 0.78 [38], 0.88 [65], 0.83–0.88 [23  ± Scores associated with stroke self-efficacy but not personal history of stroke or knowing someone with stroke [23- Scores differed by age [23], education [59], and country of residence [59± Scores increased post-intervention, but peaked 1 month later [57]. Delivery of educational leaflets had no effect on scores [58
Sungbun et al. [62] 2022  + Internal consistency: 0.84 [62   + No differences by ethnicity, education, or language proficiency at follow-up [62+ Mean scores increased from 0.57 to 7.62 following intervention (p < 0.001) [62
Tibebu et al. [63] 2021  + Cronbach’s α: 0.71 [63   ± No differences by income or education. Differences by age and rurality [63 
Workina et al. [64] 2021  + Cronbach’s α: 0.87 [64  + Knowing someone with stroke was associated with greater scores [64- Education and residence area were associated with greater scores [64 
ToolStructural validityInternal consistencyReliabilityMeasurement errorHypotheses testing for construct validityCross-cultural validityResponsiveness
Ahmed et al. [24] 2019  + Cronbach’s α: 0.83 [24+ Test-retest reliability after 2 weeks (n = 10), Spearman’s correlation: 0.954 [24 + Prior stroke associated with greater scores (p = 0.034) [24+ No significant differences by education, income, or race [24 
Ananchaisarp and Sa-a [20] 2022   + Test-retest reliability: 0.9 (prevention), 0.7 (warning symptoms) [20 ? Being unaware of stroke risk associated with greater scores [20- Greater income associated with higher scores (p = 0.04) [20 
Das et al. [25] 2016   ± Similar responses (ICC: 0.6–0.9 for each item) in sample (n = 50) assessed separately by two reviewers [25 ± Stroke affected families had greater awareness of stroke (p < 0.001), but poorer knowledge of some risk factors [25  
FAST 112 Heroes Stroke Preparedness Questionnaire [18 ± Cronbach’s α: 0.67–0.73 [18  + Knowing someone with stroke associated with increased scores (p < 0.001) [18- Master or PhD education (vs. other) associated with greater scores (p = 0.018) [18+ Median scores improved after intervention (p < 0.001) [18
Kaddumukasa et al. [26] 2015  + Cronbach’s α: 0.7 [27  ± Mean scores were similar between high and low risk participants (p = 0.72) [27± No differences in scores by sex (p = 0.89). Scores greater for individuals with secondary or university education (p < 0.001) [27 
Knowledge of Stroke Risk survey [28 ± Cronbach’s α: 0.70 [28], 0.45–0.80 [29  ± Family history of stroke had no effect on scores but was recognised as a risk factor (p < 0.05) [28 + Mean scores increased by 13% in intervention (vs. 5% in control) [29
Deepthi et al. [30] 2022  + Cronbach’s α: 0.7 [30   - Scores not comparable with other studies from India [30 
Nigat et al. [31] 2021  + Cronbach’s α: 0.71 [31  ? Long-standing hypertension (>5 years vs. ≤5 years) associated with increased scores [31- Scores greater for educated individuals able to read and write and those from urban (vs. rural) locations [31 
Oh and Yang [32] 2021  + KR-20: 0.74–0.91 [32  - Scores similar between patients with and without pre-existing vascular disease [32± Scores differed by age (p = 0.12) and education (p < 0.001), but not income level (p = 0.78) [32 
Salam et al. [17] 2022  + Cronbach’s α: 0.92 [17    + Mean scores increased post-intervention (β: 1.1–1.4; p < 0.001) and were sustained at 2 months (β: 0.8–0.7; p < 0.001) [17
Singhard et al. [33] 2011  + KR-20: 0.95 (personal communication)   ? Being aware of one’s own stroke risk did not influence scores [35+ No difference in scores based on age, gender, living situation, and education level [34 
Stroke Action Test (STAT) [21? EFA KMO: 0.87 (Bartlett’s p < 0.001) Four factors explained 48.35% of the total instrument variance [42+ Cronbach’s α: 0.83 [21], 0.85 [43], 0.89 [42], 0.91 [36+ Test-rest reliability: 0.86 [42 + Medical students had higher scores than general public (p = 0.011) [21]. Experience of stroke associated with greater scores among general public (p < 0.001) [21? Scores were lower in Hispanic (vs. non-Hispanic White) people. Scores differed by ethnicity [51]. Scores increased in people with 0–11 years of education (vs. higher) [40+ Scores increased following interventions [38, 39
Stroke Action Test (Adapted STAT) [19? ECV: 25.5–73% (Children) and >45% (Parents) [19± Cronbach’s α Children (0.43–0.87) [18] Parents (0.73–0.75) [19 + Increase in scores in parents (50–80% correct) reported to be a clinically important difference [19+ Scores increased following educational intervention versus control [19? No difference in scores based on health literacy [19+ Absolute improvement of 55% in children receiving intervention versus 0% in control group, immediately after program (p < 0.001) [19
Stroke Knowledge Assessment Tool [45 + Cronbach’s α: 0.78–0.83 [45+ Test-retest reliability after 30 days (n = 118), ICC: 0.79 [45    
Stroke Knowledge Questionnaire [46  - Test-retest after 4 weeks (n = 40), ICC: 0.66 [46   - Scores similar after intervention (potentially underpowered) [47
Stroke Knowledge Test [22? Item difficulty and discrimination indices examined. Discrimination ≥0.375 ensured for each item [22± Cronbach’s α: 0.65–0.69 [60], 0.65–0.70 [22], 0.78 [53], 0.88 [51], KR-20: 0.58 [48+ Immediate test-retest (n = 25), Pearson’s correlation: 0.82 [22 + Scores greater in those with a family history of stroke [22- Scores differed by age, education, insurance coverage, location, smoking status, alcohol intake, and comorbidities [51+ Scores improved following delivery of stroke information (p < 0.001) but not non-stroke information [22
Stroke Awareness Questionnaire [54 + Cronbach’s α: 0.84–0.86 [54  - Scores similar between people with and without stroke risk [55  
Stroke Knowledge and Sources of Knowledge Questionnaire [54 + KR-20: 0.83–0.84 [29]   - Scores similar between people with and without stroke risk [55  
Stroke Literacy Scale [56 + KR-21:Family (0.87–0.89) [56]; People at high risk of stroke (0.90) [56  - Scores not associated with stroke pre-hospital delay behaviour intention [56  
Stroke Preparedness Vignettes [23 + Cronbach’s α: 0.78 [38], 0.88 [65], 0.83–0.88 [23  ± Scores associated with stroke self-efficacy but not personal history of stroke or knowing someone with stroke [23- Scores differed by age [23], education [59], and country of residence [59± Scores increased post-intervention, but peaked 1 month later [57]. Delivery of educational leaflets had no effect on scores [58
Sungbun et al. [62] 2022  + Internal consistency: 0.84 [62   + No differences by ethnicity, education, or language proficiency at follow-up [62+ Mean scores increased from 0.57 to 7.62 following intervention (p < 0.001) [62
Tibebu et al. [63] 2021  + Cronbach’s α: 0.71 [63   ± No differences by income or education. Differences by age and rurality [63 
Workina et al. [64] 2021  + Cronbach’s α: 0.87 [64  + Knowing someone with stroke was associated with greater scores [64- Education and residence area were associated with greater scores [64 

COSMIN, COnsensus-based Standards for the selection of health Measurement Instruments; ECV, Explained Common Variance; EFA, Exploratory Factor Analysis; KMO, Kaiser-Meyer-Olkin measure of sampling adequacy; KR, Kuder and Richardson Formula.

➕indicates sufficient; -, insufficient; ?, indeterminate; and ±, inconsistent. Empty cells reflect where measurement properties were not reported.

Internal Consistency

Evidence on internal consistency was determined to be sufficient for most tools (Table 3), with 20 achieving a Cronbach’s α or Kuder and Richardson Index ≥0.7 in ≥1 study cohort.

Reliability

Evidence on test-retest reliability was available for seven tools (Table 3). Three tools had an intra-class correlation coefficient ≥0.7 [20, 25, 45], one had a high Spearman’s correlation coefficient ≥0.7 [24], and one had a Pearson’s correlation coefficient ≥0.7 [22]. The Chinese Stroke Action Test had the greatest test-retest reliability metric of 0.86 [42].

Measurement Error

Only the Adapted Stroke Action Test [19] had information reported on the smallest detectable change or minimal important difference to assess measurement error (Table 3). Authors who used this tool reported an increase in scores (from 50% to 80%), representing a clinically important difference. An indeterminate rating was assigned for this tool as evidence was not provided on how this minimal important difference was derived.

Hypotheses Testing for Construct Validity

The quality of hypotheses testing for construct validity varied between tools (Table 3). The Stroke Action Test [21] and Stroke Knowledge Test [22] had the most rigorous experiments undertaken to test hypotheses for construct validity. For the Stroke Action Test [21], scores were shown to be greater among medical students (vs. the general public), the general public with experience of stroke (vs. no experience), and among medical students following education. Notably, Stroke Knowledge Test [22] scores were shown to improve following exposure to stroke information (vs. non-stroke information), and were greater among people with (vs. without) a family member with stroke. In contrast, an inverse relationship was observed in several studies, whereby having pre-existing stroke, risk factors for stroke, or knowing someone with stroke, resulted in poorer stroke knowledge [20, 23, 25, 27, 28, 32, 55].

Cross-Cultural Validity

For cross-cultural validity (Table 3), few tools were shown to consistently measure stroke knowledge, irrespective of population socio-demographics (e.g., age, sex, ethnicity, income, education). The influence of these factors on stroke knowledge is unclear and may differ between regions and contexts. Therefore, a rating of “indeterminate” was applied in most cases where information on cross-cultural validity was reported.

Responsiveness

Evidence on responsiveness was available for nine tools, where stroke knowledge was compared before and after an intervention. In six of these studies [17‒19, 22, 28, 62], stroke knowledge was found to improve immediately following an educational intervention, providing evidence of responsiveness. Unexpected findings were reported for the Stroke Preparedness Vignettes [23], where scores were greater at 1 month, compared to immediately following delivery of educational leaflets about stroke [57, 58]. This may reflect a delayed effect of the intervention, which was delivered by post, or could reflect underlying measurement bias.

In this systematic review, we comprehensively evaluated the content validity, feasibility, and measurement properties of tools used to assess stroke knowledge in the published literature since 2015. Our review updates findings from an earlier systematic review [10] and provides new details about the measurement properties of tools based on the COSMIN criteria. Although all tools had data available to evaluate at least one COSMIN measurement property, no tool had data on all properties. Data reported for many tools resulted in COSMIN ratings of “insufficient,” “indeterminate,” or “inconclusive,” as per the pre-specified COSMIN thresholds. The Stroke Action Test (STAT) [21] and the Ahmed et al. 2019 [24] tools received the greatest number (n = 4) of “sufficient” ratings for COSMIN measurement properties. While the Stroke Knowledge Test [22] received one fewer “sufficient” rating, this was the only tool to meet all three components of content validity: relevance, comprehensiveness, and comprehensibility. Consequently, the Stroke Knowledge Test [22] remains the best available tool for assessing stroke knowledge in the current peer-reviewed literature, but lacks measurement properties for endorsement as a gold standard. Ultimately, the current diversity of tools and lack of a gold standard prevents reliable measurement of stroke knowledge, and limits comparisons from being made between different studies, populations and contexts.

In terms of content, the Stroke Knowledge Test [22] had validation data available for 6/7 measurement properties, and was rated “sufficient” based on the COSMIN criteria for reliability, responsiveness, and hypotheses testing for construct validity. Additional validation studies are needed to conclusively determine the structural validity, internal consistency, measurement error, and cross-cultural validity of this tool. Given the Stroke Knowledge Test [22] was developed almost two decades ago, some questions are outdated and should be updated, tested, and validated in a contemporary cohort. For example, statistics relating to stroke incidence and risk factors no longer reflect current disease epidemiology. Opportunities may also exist to integrate new questions to educate the community about the availability of new stroke treatments (e.g., mechanical thrombectomy) and services (e.g., telehealth). As part of the Love Your Brain trial (Medical Research Future Fund Grant #2015976), we plan to update, test, and validate the Stroke Knowledge Test [22] in a contemporary Australian cohort. This will maximise our ability to reliably measure stroke knowledge before and after a digital health education intervention.

The Stroke Action Test [21] was the most commonly used tool in the literature post-2015. This tool was found to be highly feasible (accessible at no cost and fast to complete) and had “sufficient” ratings for 4/7 measurement properties. However, in our content validity assessment, the tool scored poorly in comprehensibility and comprehensiveness. Despite the importance of understanding and managing one’s own risk factors for prevention of stroke [65], this tool only covered concepts related to recognising acute “warning signs and symptoms” and seeking “medical action/response.

Strengths

We followed best-practice standards by pre-specifying our methods in a registered protocol prior to searching the published literature. Our search strategy was based on the earlier systematic review by Hou et al. [10] with refinements from a medical librarian for expansion to six scientific databases to maximise comprehensiveness. The use of forward and backward citation searching, and contact attempts to >50 corresponding authors, maximised the capture of relevant tools. We applied no language restrictions and a total of 19 tools had versions administered in non-English languages, improving the representativeness and generalisability of our findings. Aspects of our review were guided by COSMIN methodology to increase the objectiveness and scientific rigour of our data extraction and appraisal of measurement properties and content validity. Bias was reduced by employing two or more reviewers for the screening, citation searching, and appraisal procedures.

Limitations

Limitations include our inability to examine criterion validity due to lack of a gold standard stroke knowledge tool. There were limited studies published on the initial development and validation of the tools. Consequently, our team developed a structured checklist which incorporated elements of the COSMIN checklist to appraise content validity. The assignment of ratings for hypothesis testing and cross-cultural validity required assumptions to be made on the expected (vs. actual) strength and direction of associations with other variables (e.g., age, sex, ethnicity, education, income). Although authors were contacted at least twice, we were unable to obtain copies of 22 stroke knowledge tools identified. However, only 10 of these tools were cited ≥10 times in the literature (Google Scholar as at 18 August 2023), minimising the potential impact of selection bias. As our search strategy excluded grey literature, we must acknowledge the potential for publication bias in our findings. Lastly, since conducting our search strategy on 1 March 2023, one additional potentially relevant article has been published using another unique stroke knowledge tool [66]. However, this tool only had validation data on internal consistency and is unlikely to represent a gold standard.

Future Directions

Research is needed to develop and validate an internationally accepted tool so that stroke knowledge can be accurately measured and compared between populations, over time, and before/after educational interventions. This review highlights a void in the literature on several critical measurement properties of stroke knowledge tools, including structural validity, measurement error, reliability and responsiveness. The most commonly used tools in our review, the Stroke Knowledge Test [22] and Stroke Action Test [21], are almost two decades old, highlighting the need for review and validation of content and measurement properties in a contemporary cohort. Notably, many of the questions present are country-specific and may not be generalisable to all contexts. For example, the Stroke Action Test [21] may be less valid in countries with reduced availability of ambulance services. Further, many tools comprised equally weighted questions, despite certain questions being more pertinent for stroke prevention than others. Opportunities also exist to develop and validate shortened versions of these tools to increase the utility and feasibility of more routine screening of stroke knowledge in time-limited settings, such as during healthcare visits. International collaboration is required to ensure that a gold standard tool for assessing stroke knowledge is valid and reliable in a diverse range of languages and cultures.

We systematically reviewed the literature to identify tools for assessing stroke knowledge and appraised their content validity, feasibility, and measurement properties. A lack of uniformity in the assessment of stroke knowledge was identified, with many tools lacking validated content or studies on measurement properties. While the Stroke Knowledge Test was the most comprehensive tool identified, data were not available for several important measurement properties which reduced the suitability of this tool as a gold standard. Opportunities exist to develop and validate an internationally acceptable and culturally appropriate tool for measuring stroke knowledge.

We wish to acknowledge Penny Presta (Librarian from Monash University) for assistance in refining the search strategy. We also thank Samuel Kunnel for contributions to the background literature review during his Honours project in 2022. We also appreciate the authors of each study for kindly providing their stroke knowledge tool for evaluation in our systematic review. We thank Naputsamohn Ib Junpiban (Queensland University of Technology) for translating one stroke knowledge tool from Thai to English.

This article does not contain any studies with human or animal subjects performed by any of the authors.

M.F.K. is a member of the Australian Stroke Clinical Registry Management Committee. A.G.T. is a previous Board Member of Stroke Foundation; M.R.N. and M.F.K. are members of the Research Advisory Committee of Stroke Foundation; S.L.G. is Chair of Stroke Foundation’s Stroke Prevention Advisory Committee and member of their Clinical Council. M.F.K. reports receiving educational grant from Amgen Australia and GSK outside the submitted work. L.L.D. reports receiving educational grant from GSK outside the submitted work. M.R.N. reports membership of a Novartis lipids advisory board outside the submitted work. All other authors report no conflicts.

M.F.K and S.L.G report receiving research fellowship support from the National Heart Foundation of Australia (105737, 102061). M.F.K, S.L.G, A.G.T, and M.R.N. report receiving Synergy Grant funding from the National Health and Medical Research Council of Australia (STOPstroke; 1182071). M.F.K., S.L.G., M.T.O., J.C., T.P., A.G.T., M.R.N., and H.T.P. report receiving a project grant from the Medical Research Future Fund (Love Your Brain; 2015976).

L.L.D. was responsible for screening articles, data extraction, formal analysis, and writing the original draft. C.B. was responsible for the search strategy, screening articles, and verifying the extracted data. R.F.P. was responsible for supervision. M.F.K. and S.G. were responsible for funding acquisition. A.S. and K.V. were responsible for citation searching. All authors contributed to the conceptualization, methodology and were responsible for reviewing and editing the manuscript for intellectual content.

The data that support the findings of this study are available from the corresponding author (L.L.D.), upon reasonable request.

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