Search results

A total of 1044 articles were retrieved via database searching. After removing duplicates and screening for appropriateness, a total of 59 publications were determined relevant (Table S1 Additional File 1). A detailed search and selection strategy is presented in Fig. 1.

Fig. 1

PRISMA flow diagram and selection of relevant articles

Characteristics of relevant literature

More than half of the selected articles were published in the last five years, thus representing the latest research in the field of dementia knowledge while also capturing research published in earlier time periods (Fig. 2).

Fig. 2

Distribution of the selected papers according to year of publication

The final literature sample spans over twenty-eight countries across five continents, with the European region showing the highest representation (Fig. 3). The USA leads with nine publications, followed by Australia and the UK. Notably, dementia knowledge is significantly less explored in low- and middle-income regions than in high-income countries, a trend that has persisted over time, as highlighted in a 2018 systematic review [25].

Fig. 3

A Color-coded map depicting the number of publications per country

Observed methodological variability in studies of dementia knowledge

The literature revealed significant variability in approaches to studying dementia knowledge, confirming our hypothesis. Differences emerged regarding the definition of ‘dementia knowledge,’ survey methodologies, and factors hypothesized to influence knowledge. The rest of this section offers an analysis of the observed characteristics of existing research articles on communal dementia knowledge.

Dementia knowledge as a multi-domain construct

Dementia knowledge refers to the beliefs and information individuals possess about the disease. Thus, it can be described as a multi-domain construct in which the different domains have varying levels of importance depending on the target group. Our analysis identified eight key domains of dementia knowledge: (1) General Knowledge, (2) Etiology, (3) Epidemiology, (4) Disease Course and Life Impact, (5) Assessment and Diagnosis, (6) Symptoms, (7) Care and Management, and (8) Risk Factors and Protective Behaviors. “General knowledge” is defined as non-specific knowledge about dementia that conveys general facts about the condition usually considered as “common knowledge”. Some questions that fall in the category of “general knowledge” would be “Which part of the body is primarily affected by dementia?”, “Is dementia curable?”, “Is dementia a normal part of aging?”.

The most frequently addressed domains were ‘Risk Factors and Prevention,’ followed closely by ‘Symptoms’ and ‘Care and Management.’ Conversely, ‘Etiology’ and ‘Epidemiology’ received less attention, particularly in studies involving non-specialized populations (Fig. 4). These results reflect the social importance of risk factors and symptoms literacy for the general population. Conversely, domains representing specialized knowledge (etiology, epidemiology, diagnosis) are more relevant to medical professionals.

Fig. 4

Number of publications addressing each of the dementia knowledge domains

Multi-domain surveys of dementia knowledgeApproaches to multi-domain dementia knowledge surveys

This section outlines several approaches to surveying dementia knowledge: (1) reusing validated instruments, (2) partially reusing validated instruments, and (3) creating custom instruments. The appropriateness of each approach depends on several factors including data acquisition methods, target sample characteristics, study length, and required depth of knowledge.

On the use of validated multi-domain instruments for surveying dementia knowledge

Validated multi-domain instruments are questionnaires designed to assess various aspects of dementia knowledge, including at least one item per domain. These instruments are convenient due to their proven efficacy and compatibility with data from previous studies. However, their comprehensive nature often results in lengthy questionnaires, which can be burdensome for participants, especially during telephone surveys or as part of larger studies. While aiming to quantify overall dementia knowledge, these instruments may provide insufficient depth in each domain, leading to potential ceiling effects in more knowledgeable groups. The variability among instruments can be illustrated by comparing the most commonly used multi-domain scales within our literature sample, such as the Alzheimer’s Disease Knowledge Scale (ADKS) [13], the Dementia Knowledge Assessment Scale (DKAS) [15, 26], the Dementia Knowledge Questionnaire (DKQ) and finally, the Dementia Knowledge Assessment Tool (DKAT2) [27] (Table 1). For each instrument, differences can be observed in the number of questions, response formats, domain coverage, and content relevance.

Table 1 Brief description of the most used dementia knowledge scales among the selected publications (diverse population). Knowledge domains are numerically represented under the following mapping: (1) general knowledge; (2) etiology; (3) epidemiology; (4) disease course and life impact; (5) assessment and diagnosis; (6) symptoms; (7) care and management and finally; (8) risk factors and protective behaviors

We observed a preference towards ADKS and DKAS compared to other scales within the sampled literature. This observation is in line with previous studies as a systematic literature review evaluated the psychometric quality of four dementia knowledge scales (ADKS, DKAS, DKAT2 and Dementia Knowledge 20 (DK-20) [28]) and confirmed the superior psychometric properties of ADKS and DKAS compared to the other two scales [29]. The similarity in performance between ADKS and DKAS is not a coincidence as there is a level of correlation between the two scales, suggesting that both measure a relevant knowledge construct [30]. However, ADKS seems inferior to DKAS in terms of ceiling effect due to the dichotomous format of the responses (True/False), especially in more knowledgeable samples. Furthermore, DKAS has better construct validity and is shown to perform better in large cohorts [30].

In summary, reusing validated instruments is strongly encouraged due to the demonstrated efficacy of the instruments and the ensured comparability with other studies using the same instrument. However, several caveats need to be considered before settling on an existing dementia knowledge instrument. First, while many dementia knowledge scales cover a relatively broad scope of knowledge domains, the depth of the examined knowledge may substantially vary between instruments. Furthermore, dementia knowledge scales vary in terms of specialization, psychometric qualities, length, and complexity. Next, the availability of particular scales may pose additional difficulties in reusing existing instruments. For instance, obtaining permission for use is often a lengthy process and in some cases acquiring permission may prove to be impossible, thus creating an additional set of obstacles for researchers. Another caveat worth consideration is the cultural variation occurring across communities and nations. Cultural variations may render instruments inapplicable to certain groups, thus the choice of a validated instrument should be guided by the characteristics of the studied population. Validating the psychometric properties of existing instruments in various populations is a crucial step in limiting the possible implications of this issue. Similarly, when translation or adaptations are imperative one should ensure that the properties of the instrument remain intact and it still measures the intended constructs.

Finally, dementia knowledge scales reflect the current body of knowledge relevant to the time of their development. Therefore, if not periodically revised, existing knowledge scales may become outdated or fail to incorporate the latest advancements in science and medicine. These variations highlight the need for careful consideration when selecting an instrument for dementia knowledge assessment.

Partially reusing existing multi-domain instruments

Validated multi-domain instruments can be adapted to meet the specific needs of a study, allowing for tailored assessments of dementia knowledge. For instance, when including a dementia knowledge survey within a larger questionnaire, a more compact design may be necessary. Techniques such as reusing the subdomain structure of existing scales can be effective in these cases [31]. In this scenario, the internal structure of the instrument remains intact but the number of the questions within each section or the phrasing of the questions may be altered. A similar is approach preselecting questions from an existing instrument to create a more compact representation [32]. Both of these approaches utilize an existing instrument by significantly augmenting its structure or content which may distort the internal properties of the scale and lead to drift from its measured construct.

Another approach is adapting existing instruments according to the needs of a study [33, 34]. In such cases, small augmentations are introduced, but the overall structure and content of the instruments remain intact. While modifications might seem minor, they can impact the psychometric properties of the scale. Thus, caution is warranted when making adjustments.

Although modifying existing instruments can be a straightforward solution, it’s essential to recognize that their psychometric properties and overall effectiveness may differ significantly. This raises questions about data compatibility, as the constructs measured might not align between the adapted and original scales.

Designing custom instruments for surveying dementia knowledge

In studies targeting specific populations, researchers often create custom instruments tailored to their sample’s characteristics. Custom instruments typically employ a criterion-referenced strategy, using items drawn from existing literature and previously validated scales. While the resulting items may lack high internal reliability, this strategy allows for flexibility in assessing knowledge on specific topics. For example, Jang et al. created a dementia knowledge instrument for Korean American elders by combining questions from several established instruments [35]. The same approach was utilized by Isaac et al. [36] in researching dementia knowledge among adolescent students, and by Arai et al. [37] and Zülke et al. [38].

Another approach to creating custom scales is generating custom items on face validity [39,40,41,42,43,44]. However, generating items based on face validity can have drawbacks. The process often lacks systematic evaluation, which can be mitigated by involving a broader team of stakeholders in content validation [42]. Additionally, if the custom instrument diverges significantly from established measures, the resultant data may be incompatible with previously collected datasets, making comparisons challenging.

Using hybrid approaches

While these three strategies can be used independently, hybrid approaches may also offer benefits. For instance, Nielsen and Waldemar [45] supplemented the Dementia Knowledge Questionnaire (DKQ) with two questions from the Alzheimer’s disease Awareness Test (ADAT) to address the stigma associated with Alzheimer’s disease. In Croatia, researchers enhanced the Alzheimer’s Disease Knowledge Scale (ADKS) with extra questions on differential diagnosis, pathogenesis, and epidemiology [46]. Similarly, another work supplemented the Dementia Knowledge Assessment Scale (DKAS) with seventeen questions pertaining to different types of dementia, mild cognitive impairment, genetic risk for Alzheimer’s disease, and the impact of dementia on driving [47].

Lastly, it is important to highlight the role of qualitative approaches in studying dementia knowledge. For example, one study used interviews to gather family members’ perspectives and examine whether they view dementia as a terminal condition [48]. Although qualitative methods are less commonly used, we believe that combining quantitative data with qualitative insights can deepen our understanding of dementia knowledge since this approach offers a direct look at how people perceive and conceptualize the disease.

Single-domain surveys of dementia knowledge

Single-domain surveys focus on specific areas of dementia knowledge, offering benefits for assessing higher or more specialized knowledge levels. Thus, this approach is particularly popular when targeting non-general samples or surveying non-conventional knowledge.

This section discusses studies that have concentrated on individual knowledge domains or have assigned separate tasks for assessing specific areas.

Recognition of dementia symptoms

Recognizing dementia symptoms is crucial for early diagnosis and treatment, leading to better outcomes for patients.

In 2003, P. Werner studied public knowledge of Alzheimer’s disease symptoms and its correlation with help-seeking behavior. Participants rated 15 symptoms on a 5-point Likert scale, distinguishing between Alzheimer’s and depressive symptoms [49]. Another example can be found in a work by Low and Anstey [50] where knowledge about dementia symptoms was assessed by using clinical vignettes. Each vignette variation described a character that had symptoms meeting the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), criteria for Alzheimer’s disease. The lead character in the vignette was described as having mild (50%) or moderate (50%) symptoms of dementia, and as being either male (50%) or female (50%). Participants’ recognition of dementia was determined by asking them what, if anything, was wrong with the lead character in the vignette. The same vignettes were reused in a following study by Low et al. [51] as well as a replicating study published by Nagel et al. in 2021 [52]. Vignettes were also used by Blay and Peluso [53] in their study on the Brazilian public’s ability to recognize Alzheimer’s disease. A similar approach was used in another study, where participants were asked the following question: “What word or words would you use to describe an older adult experiencing memory loss and difficulties with thinking, problem-solving, and language, so much so it affects their ability to perform everyday activities?” [54].

While clinical vignettes offer a more realistic approach to studying dementia recognition capabilities, note that recognition in real life might be poorer than the reported results from vignette tasks. This might be, at least partially, due to the subtle nature of dementia progression. Indeed, family members may not notice subtle changes in cognitive functioning, or they may often attribute them to normal ageing [25, 55].

Recognition of risk factors and dementia causes

Knowledge about dementia risk factors and dementia causes is usually studied by using identification lists. In these tasks, participants are presented with a list of items and asked to rate the contribution of each factor to the development of dementia [50, 52, 53]. There may be variations in the item lists depending on the additional factors of interest. For example, Nagel et al. [52] compiled a list containing true dementia contributors, emerging risk factors, supported by partial evidence and factors without evidence, based on popular beliefs. In their work, Blay and Peluso [53] included several factors related to spiritual beliefs and religion, such as “Lack of faith in God”, “Evil eye”, “Fate”, and Low and Anstey [50] added “Weakness of character” and “Laziness” to measure negative perceptions of persons with dementia. Similarly, others explored beliefs about protective behaviors as a natural continuation to the knowledge about risk factors [39, 56, 57]. Zheng et al. [57] presented a randomized list containing risk factors and protective behaviors and asked the participants to mark which items increase the risk of dementia and which lower the risk. A similar approach was used also by Kjelvik et al. in a study from 2022 [39]. Item lists containing etiological factors, protective and risk factors were used in several other works [54, 58,59,60].

The great benefit of list-based tasks is that they can accurately reflect the current knowledge about dementia. For instance, while the non-modifiable risk factors for dementia have been firmly established over the years, strong empirical evidence for modifiable risk factors has only been demonstrated in the past ten years, and the list of potential risk factors is ever-growing. The Lancet report, first published in 2017 [61], identified 9 modifiable risk factors for dementia (less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, and infrequent social contact), the next revision of the report, published in 2020 [11], added another three factors (excessive alcohol consumption, head injury, and air pollution) and with the recently published report from 2024 [62], the list grew with two new additions (untreated vision loss and high LDL cholesterol), accounting for an up-to-date total of 14 modifiable risk factors.

Reflecting the local cultural beliefs surrounding disease genesis is equally important in identifying misconceptions and knowledge gaps about dementia. A prominent example is a study from 2005, exploring knowledge and attitudes towards mental disorders in Nigeria, according to which 32.3% of the respondents attributed “possession by evil spirits” as a cause of mental disorders. Similar findings were reported by Blay and Peluso [53]. Their study on the Brazilian population reported that 68.8% of the participants agreed that a “lack of faith in God” may be related to Alzheimer’s disease and 25.8% agreed that “evil eye” may be a contributor to Alzheimer’s dementia. Religious explanations about dementia were also reported by some South Asian carers who viewed dementia as demons or God’s punishment [55].

Factors influencing dementia knowledge

All studies typically collect sociodemographic data, such as age, gender, education, marital status, and income. Research on dementia knowledge follows this standard but often examines additional factors that may influence knowledge levels. Identifying influencers and correlates of dementia knowledge is crucial, as this analysis can reveal groups at risk for low literacy. The following sections discuss additional factors impacting dementia knowledge.

Prior experience with dementia

The literature identifies three types of direct experiences with dementia: having a diagnosed with dementia relative, providing care to someone with dementia, and working in a profession involving dementia patients. Some studies use general questions like ‘Have you ever known someone with dementia?’ However, this approach fails to differentiate between relatives and professionals, which is critical as professional experience often correlates with greater dementia knowledge and understanding [13, 47, 57, 60, 63, 64].

Sources of information

Dementia knowledge correlates extend beyond sociodemographic factors and personal experience. The sources of information individuals use can significantly affect their dementia knowledge [59, 65], making both the quantity and quality of these sources important to consider.

Perceived threat

Perceived threat is another important factor influencing dementia knowledge. Another factor that may be considered is the levels of perceived threat. Stronger concerns about developing dementia may influence levels of knowledge about symptoms, risk factors and protective behaviors. Thus, individuals with stronger concerns about developing dementia may exhibit greater knowledge of symptoms, risk factors, and protective behaviors. However, this awareness does not always translate into proactive preventive actions [34, 66]. Interestingly, this knowledge does not necessarily equate to exercising preventive behavioral practices [