Abstract
Background
There is growing appreciation of the role health literacy plays in population health and health care design. Health literacy encompasses an individual’s capacity to manage their health and the responsiveness of the health system. Our aim was to identify the health literacy strengths and challenges in an Australian cohort living with motor neurone disease (MND), including both people living with the disease and their carers.
Methods
This study used the Health Literacy Questionnaire and eHealth Literacy Questionnaire for health literacy assessment. Using a secure online platform, an anonymous survey was disseminated which included demographic data and clinical measurements. Descriptive statistical analysis and cluster analysis were employed to describe the sample and to identify different health literacy patterns in subgroups of people living with MND and their carers.
Results
A total of 227 people participated (171 people living with MND and 56 carers). Cluster analysis generated fifteen cluster profiles for the cohort living with MND and seven cluster profiles for carers. The variability and potential significance of patterns of health literacy strengths and challenges within the MND community are described. There was extensive diversity within the sampled population, with a mix of sociodemographic backgrounds across each cluster profile.
Conclusions
The health literacy cluster profiles created from this study provide insight into the full spectrum of where the challenges and strengths exist for individuals and subgroups of people managing this fatal disease. The results from this study pave the way for generating system wide interventions that address health literacy diversity, to create more enabling health care environments for all those affected by MND.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-025-12998-x.
Keywords: Motor neurone disease, Amyotrophic lateral sclerosis, Health literacy, Co-design methodology, Health literacy questionnaire, e-Health literacy questionnaire
Background
Motor neurone disease (MND) including the most common phenotype, amyotrophic lateral sclerosis (ALS), is a rapidly progressive and fatal neurodegenerative condition, characterised by progressive muscle weakness with death often resulting from respiratory failure [1]. It is not uncommon, with a lifetime risk of developing ALS/MND estimated to be 1 in 300 [2] and a global prevalence estimated to be 4.42 per 100 000 people [3]. The importance of health literacy for people living with MND (plwMND) and their families is paramount given the relentlessly progressive nature of this condition, the volume of new health information delivered at diagnosis and the complex decision-making involved in management [4].
Health literacy is a concept that developed in the 1970s [5]. It has been defined as a “multi-faceted mechanism that includes people’s knowledge, confidence and comfort – which accumulate through daily activities, social interactions, and across generations – to access, understand, appraise, remember and use information about health and health care, for the health and wellbeing of themselves and those around them” [6, 7]. Health literacy is a subject that is becoming increasingly explored in research and clinical practice, as it is inextricably related to health outcomes and health equity. Lower health literacy is linked with less advantageous health choices, reduced ability to self-manage illness, increased hospitalisation, morbidity and premature death [8]. It has also been associated with delayed end-of-life care planning in other neurological diseases like Parkinson’s disease [9].
In the case of MND, which is an evolving multi-system disease with progressive disability, plwMND and carers are confronted with an urgent need to understand and access a great deal of new and often complex information, appraise this information, and then make a wide range of decisions [10]. The role of advocacy is often assumed by carers, who find themselves suddenly learning a complex new disease and how to navigate a healthcare system that may be unfamiliar to them. Furthermore, the journey for each person with MND is individual. There are time-sensitive, and often nuanced management decisions to be made, such as non-invasive ventilation and nutritional support with a gastrostomy [10]. These decisions require significant information support, ideally from members of a specialist multi-disciplinary team. A proactive, early intervention approach to these shared management decisions is now considered best practice [4]. This approach aims to give plwMND agency over management decisions that impact both symptom management and potentially life-prolonging interventions, and to limit recourse to late, and often futile treatments or hospitalisation [10, 11]. Therefore, health literacy responsive healthcare settings that support people to reach timely, informed decisions that reflect their individual contexts, needs and preferences, will provide better and more equitable support to all plwMND and carers.
There is a paucity of research identifying health literacy strengths and challenges among plwMND. A qualitative study was done in Melbourne, Australia in 2022 involving thematic analysis of semi-structured interviews with 19 plwMND and 15 carers through the lens of health literacy [9]. This study provided the first insights into the health literacy challenges of plwMND and their carers. One of the key messages from this explorative study was that health professionals working in MND specialist clinics should have a better appreciation of the diversity of individual information needs [9]. It highlighted the need for an individual and tailored clinician’s approach to the provision of health information for each plwMND. Further work by Hobson et al. identified the preferences of health information delivery for plwMND, which included being able to receive information from their trusted healthcare professional due to concerns over the reliability of online information [12]. Similar health information preferences were also found in a similar Korean study that focused on carers and plwMND [13].
The aim of this study was to identify the health literacy strengths and challenges for plwMND in Australia across different demographic settings. Given the highly diverse health information needs within the population, we took a contemporary approach to try and understand the health literacy strengths and challenges. Consequently, to gain a detailed understanding of this population to inform future interventions, we applied cluster analysis and generated vignettes in line with the Ophelia (Optimising Health Literacy and Access) [14, 15] process to advance health and equity in this field. This project represents an important starting point to understand the needs of those affected by MND and inform efforts to improve the delivery of health care.
Methods
Study setting, participants and data collection
The project was coordinated by Calvary Health Care Bethlehem (CHCB), a multidisciplinary MND clinic in Melbourne, Australia and guided by the Health Literacy Development team at the Centre for Global Health and Equity, Swinburne University of Technology, also in Australia. An invitation to participate in the study was disseminated to plwMND via email through multiple avenues, including contact from the research team at CHCB and MND Australia state associations, which is the national peak body supporting plwMND. PlwMND included those with a diagnosis of amyotrophic lateral sclerosis (ALS). Those recruited prior to the COVID-19 pandemic restrictions in 2020 provided their electronic or written consent prior to completing the survey. In 2021, a protocol amendment changed the survey to being online and collected anonymously.
The demographic survey data included age, sex, home postcode, primary language spoken, living arrangements (home alone, with family or carers), education and occupation. The home postcodes were used to identify the classification of residence in terms of remoteness relating to access to services [16]. The postcodes also help classify the economic and social conditions of people based on the Index of Relative Socio-economic Disadvantage (IRSD) generated by the Australian Bureau of Statistics (ABS) [17]. Psychological status was measured by the Depression, Anxiety and Stress Scale – 21 (DASS-21), which is a self-reported scale with 21 questions designed to measure emotional states of depression, anxiety and stress [18]. The survey for plwMND also included the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-r), which is a validated questionnaire-based scale employed widely in MND clinical trials that measures limitations on physical function to perform daily activities in plwMND [19]. The ALSFRS-r is scored from 0 to 48 points, where higher scores indicate retained physical function across activities of daily living, and lower scores closer to 0 indicating significant physical disability in limb, bulbar and respiratory function. Carers completed the Zarit Caregiver Burden Interview (ZBI-12). The ZBI-12 is a 12-item survey asking about the physical, financial, social and emotional wellbeing impact for the caregiver [20]. The Institute for Molecular Bioscience, The University of Queensland managed data capture, storage and security.
Health literacy measurements
The Ophelia process [14] applies a comprehensive health literacy needs assessment using the Health Literacy Questionnaire (HLQ) [21] to identify the strengths and challenges unique to that population. The HLQ has nine domains that describe the different personal skills and experiences that enable an individual to understand, engage and interpret health information and interact with health systems. These nine domains are listed in Table 1. In addition to the HLQ, the eHealth Literacy Questionnaire (eHLQ) [22] was used to provide specific insight into respondent’s ease of use and access to digital technologies and resources to support their health information quest. The eHLQ includes seven domains related to people’s interactions with digital technologies. See Table 1. Supplementary Table 1 provides more detail on each of the domains.
Table 1.
| HLQ Domains |
|---|
| 1. Feeling understood and supported by healthcare providers |
| 2. Having sufficient information to manage my health |
| 3. Actively managing my health |
| 4. Social support for health |
| 5. Appraisal of health information |
| 6. Ability to actively engage with healthcare providers |
| 7. Navigating the healthcare system |
| 8. Ability to find good health information |
| 9. Understand health information well enough to know what to do |
| eHLQ Domains |
|---|
| 1. Using technology to process health information |
| 2. Understanding of health concepts and language |
| 3. Ability to actively engage with digital services |
| 4. Feels safe and in control |
| 5. Motivated to engage with digital services |
| 6. Access to digital services that work |
| 7. Digital services that suit individual needs |
Respondents answered the questions based on a 4-point Likert-scale (‘Strongly disagree’ to ‘Strongly agree’) with a score range of 1–4 for the eHLQ and Domains 1–5 of the HLQ. Domains 6–9 of the HLQ use a 5-point Likert-scale (‘Cannot do or always difficult’ to ‘Always easy’) with a score range of 1–5. The HLQ and eHLQ were developed through an in-depth consultation process with a wide range of healthcare practitioners, through to people in the general population, employing a grounded approach [21]. Both questionnaires have been shown to demonstrate rigorous psychometric properties in various settings and contexts [23–28].
Data analysis
Statistical analysis was conducted using SPSS Statistics Version 29.0. Descriptive statistics were generated for the demographics, ALSFRS, ZBI-12, DASS 21, HLQ and eHLQ. For the HLQ and eHLQ scores, the expectation maximization algorithm was used for missing values imputation if a scale of 4 to 5 items had fewer than 2 missing values, and if a scale of 6 items had fewer than 3 missing values. Based on the Ophelia protocol, hierarchical cluster analysis using Ward’s method for linkage was employed to group participants with similar health literacy and digital health literacy scores [6]. Considering the final sample size, 12 variables instead of all HLQ and eHLQ domains were used for the cluster analysis. They included all 9 HLQ domains and domains 1, 4 and 7 of the eHLQ. The three eHLQ domains were chosen as they represented the three main areas of digital use: user capability (domains 1, 2 and 3), trust and motivation (domains 4 and 5) and experiences of digital services (domains 6 and 7) [22]. The method for choosing the optimal cluster solution has been previously described [29]. It aimed to minimise the variance within each domain within each cluster. It also aimed to ensure clusters with different health literacy patterns (strengths and challenges) were represented as they would require different interventions. Small clusters were included if they demonstrated a health literacy pattern that could easily be recognised among clinicians, as these small clusters might represent hard-to-reach populations. To determine the final optimal cluster solution, 3 to 16 cluster solutions were examined and discussed among the authors.
Results
Sociodemographic characteristics of participants
A total of 227 participants (171 plwMND and 56 carers) completed the survey. The demographic details of participants, both plwMND and carers are in Table 2.
Table 2.
Sociodemographic and health status characteristics of participants
| Characteristics | All participants | plwMND | Carers |
|---|---|---|---|
| Gender | |||
| Male | 122 (53.5%) | 113 (66.1%) | 9 (15.8%) |
| Female | 104 (45.6%) | 57 (33.9%) | 47 (82.5%) |
| Age mean, (SD) range | 64.2 (11.9) | 66.6 (10.3) | 57.1 (13.5) |
| 31- 87 | 31-87 | 31-85 | |
| Language | |||
| Primary Language English | 211 (92.5%) | 158 (92.4%) | 53 (93%) |
| English as a second language | 11 (4.8%) | 8 (4.7%) | 3 (5.3%) |
| Non-English speaking | 1 (0.4%) | 1 (0.6%) | 0 |
| State of residence | |||
| Victoria | 175 (76.8%) | 131 (76.6%) | 44 (77.2%) |
| Queensland | 27 (11.8%) | 20 (11.7%) | 7 (12.3%) |
| New South Wales | 10 (4.4%) | 8 (4.7%) | 2 (3.5%) |
| South Australia | 10 (4.4%) | 8 (4.7%) | 2 (3.5%) |
| Western Australia | 4 (1.8%) | 2 (1.2%) | 2 (3.5%) |
| Classification of residence* | |||
| Major cities | 166 (72.8%) | 119 (69.6%) | 47 (82.5%) |
| Inner regional area | 57 (25%) | 48 (28.1%) | 9 (15.8%) |
| Outer regional area | 2 (0.9%) | 1 (0.6%) | 1 (1.8%) |
| Remote area | 1 (0.4%) | 1 (0.6%) | 0 |
| Living in a disadvantaged area (IRSD decile 1-5)* | 57 (24.3%) | 15 (26.8%) | |
| Highest level of education | |||
| Primary or secondary school | 50 (21.9%) | 42 (24.6%) | 8 (14.0%) |
| TAFE/trade qualification | 25 (11.0%) | 22 (12.9%) | 3 (5.3%) |
| Certificate or Diploma | 54 (23.6%) | 46 (26.9%) | 8 (14%) |
| Advanced diploma/Associate degree | 14 (6.1%) | 8 (4.7%) | 6 (10.5%) |
| Undergraduate | 44 (19.3%) | 28 (16.4%) | 16 (28.1%) |
| Postgraduate | 40 (17.5%) | 24 (14.0%) | 16 (28.1%) |
| Current/Past Occupation** | |||
| Professional | 93 (40.8%) | 65 (38.0%) | 28 (49.1%) |
| Technician or Associate Professional | 13 (5.7%) | 12 (7.0%) | 1 (1.8%) |
| Clerical Support Worker | 28 (12.3%) | 17 (9.9%) | 11 (19.3%) |
| Services worker, retail or sales worker | 7 (3.1%) | 6 (3.5%) | 1 (1.8%) |
| Skilled agricultural or forestry worker | 5 (2.2%) | 5 (2.9%) | 0 |
| Craft or related trades worker | 10 (4.4%) | 8 (4.7%) | 2 (3.5%) |
| Plant and machine operator or assembler | 7 (3.1%) | 7 (4.1%) | 0 |
| Elementary Occupation | 6 (2.6%) | 5 (2.9%) | 1 (1.8%) |
| Armed Forces | 3 (1.3%) | 3 (1.8%) | 0 |
| Other | 49 (21.5%) | 41 (24%) | 8 (14%) |
| ALSFRS-r Score Range: 0 to 48 | plwMND (Total N=166, missing data = 5) | Not applicable | |
|---|---|---|---|
| 0 - 9 | 3 | ||
| 10 - 19 | 10 | ||
| 20 - 29 | 37 | ||
| 30 - 39 | 84 | ||
| 40 -48 | 32 | ||
| DASS-21 Survey | plwMND (Total N=165, missing data = 6) | Carers (Total N=54, missing data=2) | |
|---|---|---|---|
| Depression: | |||
| Normal (0-4) | 90 | 30 | |
| Mild (5-6) | 29 | 10 | |
| Moderate (7-10) | 31 | 8 | |
| Severe (11-13) | 7 | 4 | |
| Extremely severe (³14) | 8 | 2 | |
| Anxiety: | |||
| Normal (0-3) | 103 | 32 | |
| Mild (4-5) | 30 | 14 | |
| Moderate (6-7) | 17 | 2 | |
| Severe (8-9) | 8 | 2 | |
| Extremely severe (³10) | 7 | 4 | |
| Stress: | |||
| Normal (0-7) | 140 | 37 | |
| Mild (8-9) | 13 | 9 | |
| Moderate (10-12) | 9 | 4 | |
| Severe (13-16) | 2 | 2 | |
| Extremely severe (³17) | 1 | 2 | |
| Zarit Burden Interview (ZBI) | |||
| None to mild burden (0-10) | Not applicable | 11 | |
| Mild to moderate burden (11-20) | 22 | ||
| High burden (³ 21) | 21 | ||
*Classification of residence was determined based on participant’s postcode and sorted according to The Australian Statistical Geography Standard, which defines Remoteness Areas for the purposes of statistics [16]. The Index of Relative Socio-economic Disadvantage (IRSD) was generated by the Australian Bureau of Statistics (ABS) to map social and economic disadvantage according to postcodes in Australia [17]
**Current or past occupations explained: Professional e.g., Engineer; IT; medical doctor; teacher; accountant; lawyer; psychologist; librarian; other professional. Technician and Associate Professional e.g., engineer technician; lab technician; computer assistant; ship and aircraft controller or pilot. Clerical Support Worker, e.g., office clerk; customer services clerk; secretary; cashier; travel agency clerk; receptionist. Services Worker and Shop and Market Sales Worker e.g., travel guide; housekeeper; waiters; waitress or bartender. Skilled Agricultural, Forestry and Fishery worker e.g., market-oriented skilled agricultural or fishery worker. Craft and related Trades Worker e.g., extraction (mining) and building trades worker; metals worker; machinery trades worker; precision; welder. Plant and Machine Operator or Assembler e.g., stationary-plant or related-operator; machine operator or assembler. Elementary Occupation e.g., homemaker; street vendor; domestic helper, cleaner or launderer; garbage collector; agricultural, fishery; truck driver; bus/train driver
Health literacy and digital health literacy
The HLQ and eHLQ mean score for each domain is shown in Table 3. The results show that participants have on average relatively high scores for most HLQ and eHLQ domains. For the HLQ, the highest score was for ‘1. Feeling understood and supported by healthcare providers’ (mean 3.26; SD 0.58). The lowest score (mean 2.91; SD 0.63) was reported for domain 5: ‘Appraisal of Health Information’. Overall, the HLQ mean scores in this study cohort were similar to the general Australian population based on data collected in the 2018 ABS Health Literacy Survey [30].
Table 3.
Health Literacy Questionnaire and eHealth Literacy Questionnaire mean scores for all participants (plwMND and carers)–compared to the Australian Bureau of Statistics 2018 Health Literacy Survey
| All participants mean score (N = 220) (SD) [95% CI] | National ABS (N = 5790) Mean (SD) [95% CI] |
||
|---|---|---|---|
| HLQ Domains | |||
| Range: 1–4 | 1. Feeling understood and supported by healthcare providers | 3.26 (0.58) [3.18–3.33] | 3.19 (0.49) [3.18. 3.20] |
| 2. Having sufficient information to manage my health | 3.00 (0.54) [2.93–3.07] | 3.15 (0.41) [3.14, 3.16] | |
| 3. Actively managing my health | 3.07 (0.54) [3.00–3.14] | 3.10 (0.45) [3.09, 3.11] | |
| 4. Social support for health | 3.07 (0.54) [3.00–3.14] | 3.15 (0.47) [3.14, 3.16] | |
| 5. Appraisal of health information | 2.97 (0.53) [2.90–3.04] | 2.92 (0.45) [2.91, 2.93] | |
| Range: 1–5 | 6. Ability to actively engage with healthcare providers | 3.96 (0.69) [3.86–4.05] | 4.18 (0.62) [4.17, 4.20] |
| 7. Navigating the healthcare system | 3.81 (0.64) [3.72–3.89] | 4.02 (0.62) [4.00, 4.03] | |
| 8. Ability to find good health information | 3.83 (0.64) [3.74–3.91] | 4.10 (0.58) [4.09, 4.12] | |
| 9. Understanding health information well enough to know what to do | 4.03 (0.62) [3.94–4.11] | 4.29 (0.56) [4.27, 4.30] | |
| eHLQ Domains | |||
| Range: 1-4 | 1. Using technology to process health information | 2.79 (0.61) [2.71–2.88] | Not applicable* |
| 2. Understanding of health concepts and language | 2.95 (0.48) [2.89–3.01] | ||
| 3. Ability to actively engage with digital services | 2.85 (0.68) [2.76–2.94] | ||
| 4. Feel safe and in control | 2.80 (0.60) [2.73–2.88] | ||
| 5. Motivated to engage with digital services | 2.72 (0.57) [2.64–2.79] | ||
| 6. Access to digital services that work | 2.75 (0.53) [2.68–2.82] | ||
| 7. Digital services that suit individual needs | 2.51 (0.60) [2.43–2.59] | ||
*Data for the eHLQ domains not collected from the national Australian Bureau of Statistics health literacy survey
In general, the eHLQ domain scores were lower. The highest mean result was domain 2: ‘Understanding of health concepts and language’ (mean of 2.95; SD 0.48). The lowest scored was domain 7: ‘Digital services that suit individual needs’ (mean 2.51; SD 0.60).
Cluster analysis
There were fifteen plwMND cluster profiles identified in the data. A total of 166 from 171 plwMND participants who completed the questionnaire were included in the cluster analysis (5 excluded due to missing data). 54 of the 56 participants from the carers of plwMND were included, two excluded due to incomplete data. For ease of interpretation, the clusters are ordered from overall higher health literacy scores, down to lower health literacy scores: P1 to P15 for plwMND and C1 to C7 for carers of plwMND. Using the Ophelia methodology, a graded heatmap colour system was applied in the cluster profiles to accentuate the patterns of strengths and challenges exhibited by each group in a cluster, which helps interpretation of the potential experiences of the sub-groups [6]. Details of sociodemographic characteristics, MND disease severity and mental health were linked to the profile of each cluster. The number of participants who fell into each cluster varied from 1 person (Clusters P6, P8 and P14) to 38 people (Cluster P5). Figure 1 shows the cluster profiles for plwMND, with more detailed results for each of the higher frequency cluster profiles available in Fig. 2. The low frequency cluster profiles (N < 5 persons) are not presented in Fig. 2 with demographic information due to identity protection standards. The low frequency cluster profiles were:
Cluster P6: Strong healthcare support but do not have sufficient information to manage health (< 3.0% of sample)
Cluster P8: Actively managing health with good understanding of health information despite lack of healthcare support (< 3.0% of sample)
Cluster P9: Being proactive in managing health but finding the right care can sometimes be difficult and no access to trustworthy digital health services (< 3.0% of sample)
Cluster P12: Lack social and healthcare support as well as information to manage health despite being proactive in engaging with healthcare providers (< 3.0% of sample)
Cluster P14: Attempt to manage health despite lack of healthcare support and not using technology for health (< 3.0% of sample)
Cluster P15: Difficulty in proactively engaging with healthcare professionals and seeking the right care and do not trust technology (< 3.0% of sample)
Fig. 1.
Cluster analysis showing fifteen health literacy clusters of plwMND. Legend: A heatmap colour coded system is employed. For each domain, cells in the green range represent higher scores, the yellow range represents middle scores, and the red range represents lower scores. Clusters are ordered (P1 to P15) from highest average scores to lowest average scores
Fig. 2.
Health literacy cluster profiles with sociodemographic description for plwMND. Legend: Each cluster profile from P1 to P15 is described, with the mean scores from the health literacy questionnaire and e-health literacy questionnaire represented above the demographic data. The clusters with lower frequencies (N < 3.0% of total sample) are presented with their mean scores only but details on their profiles have not been included to protect potentially identifiable participants
Additionally, there were seven cluster profiles generated for the 56 carers of plwMND who participated (see Fig. 3 ). Figure 3 provides more details from cluster C3, which was the highest frequency carer cluster profile representing 44.6% of participants.
Fig. 3.
Cluster analysis showing seven health literacy clusters for carers of plwMND (N = 54) and detailed description for largest cluster C3 (N = 25). Legend: A heatmap colour coded system is employed. For each domain, cells in the green range represent higher scores, the yellow range represents middle scores, and the red range represents lower scores. Clusters are ordered C1 to C7 from highest average scores to lowest average scores
Discussion
This study identified fifteen health literacy profiles for plwMND and seven for carers of plwMND. These represent subgroups within the MND community with unique health literacy characteristics, shaped by individual life experience, education, cultural and socioeconomic context. It demonstrates that cluster analysis gives nuanced insight to health literacy strengths and challenges for this community in comparison to the presentation of HLQ average scores alone [29, 31]. As stated by the HLQ developers: “Scores on the nine scales are not combined, rather profiles showing the areas of strength and needs across the nine scales are produced. The HLQ thus provides a mapping of health literacy needs for individuals and groups of individuals. This, in turn, informs an approach to health system strengthening through a) optimising the health literacy of individuals and, b) optimising the health literacy responsiveness of organisations” [31]. Individuals within the population who may feel marginalised and have many health literacy struggles, are often hidden in standard statistical analysis and under-represented in studies seeking to explore how service improvements can be undertaken. These groups are often less engaged with research. However, with the cluster analysis we were able to identify them and their particular profiles. Given their unique patterns of health literacy, it is likely that they may not effectively engage with mainstream health care delivery services. The results also show that clusters with overall low or very low scores may still have certain health literacy strengths in some domains. This is important and such patterns reveal opportunities to build on existing strengths, informing targeted intervention approaches within a contemporary strengths-based approach to health literacy development [7].
The health literacy profiles demonstrate that there are distinct patterns of health literacy within each cluster. For example, in Fig. 2, Cluster P3 (15.1% of the sample) have high health literacy scores across all domains. This group have a high percentage (44%) of people who attended university. However, the slightly lower score in HLQ 2: ‘Having sufficient information to manage my health’, suggests that they may prefer more high-level information. This level of detailed interpretation shows that while individuals in Cluster P3 have the skills and tools to find most of the information they require, they may be looking for information that is more difficult to access, such as recent research evidence.
The most prevalent cluster profile was Cluster P5 (22.9% of the sample) (Fig. 2) where there was the largest representation of non-English speaking persons (15.8%) and 23.7% of the group lived outside the state of Victoria. The people in this cluster report feeling adequately supported at home (HLQ 4) but not always understood by their healthcare providers (HLQ 1). This may relate to language or cultural barriers for those where English is their second language. The group want to manage their health better but might have trouble appraising information or using digital technologies to find the right kind of information. Understanding their challenges with access and processing of health information related to MND, provides an opportunity to build on this group’s strong social and family involvement in their health care.
The profiles of the small clusters (Clusters P6, P8, P9, P12, P14 and P15) were not described in full in Fig. 2 to protect those potentially identifiable participants. However, it was important to include these cluster profiles as they may reflect people who are likely to be usually underrepresented in research. Exploring this theme further, these clusters had considerably lower scores in some or most HLQ and eHLQ domains. For example, people in Cluster P8 (Fig. 2) have adequate information about MND but they reported a lack of support from healthcare professionals and they find they are unable to effectively interact with healthcare professionals. Cluster P12 reported extremely little social support and healthcare support; Cluster P14 scored extremely low for use of digital technologies, and Cluster P15 has significant challenges accessing information, engaging with healthcare support and navigating the health care system. These are troubling findings, potentially representing vulnerable subgroups, pressingly in need of a more socially adaptive response from health care providers Fig. 2.
In addition, the later clusters with larger numbers of participants, notably Clusters P10 (7.8%), P11 (4.8%) and Cluster P13 (10.2%), also demonstrated lower health literacy scores across many domains. There is a trend in these clusters towards lower levels of education after secondary school, with only 12.5% having attended university in Cluster P11 and higher representation of those living in socially disadvantaged areas of Australia (50% in Cluster P11 and 41.2% in Cluster P13). Furthermore, within these clusters there were greater numbers of plwMND with more advanced disease and a trend towards higher levels of anxiety and depression. This highlights that many things can be associated with a person’s engagement with health information and care decision-making, including socio-demographic factors, past experience of the health system and potentially correctable factors such as depression or anxiety. Paynter et al. also noted that “persons living with MND and carers sought information from a range of evidence-based and lay sources, which were nuanced depending on their priorities, perceived relevancy, attitudes, and emotional readiness” [9]. Therefore, it is essential for health professionals who take the time to explore these factors with individuals in their care, to better meet their needs and support more tailored shared decisions [32].
A novel inclusion in this study is the analysis of health literacy for carers of plwMND. The largest carer cluster (cluster C3) represented 44.8% of the carers of plwMND who participated. Forty-four percent of this cluster recorded high levels of carer burden. Lower scores for HLQ 3 indicate having less time, knowledge or skills to look after one’s own health. Finding the right information on how to care for someone with MND (HLQ 5) was also a challenge. This parallels the literature on caregiver burden for MND families, which has highlighted a need for increased access to health information and respite care [33].
All of the lead clinical authors (AD, SM, FF and MMc) who work primarily with plwMND examined the clinical and social logic of the cluster profiles. It was evident that from their combined experience that the cluster profiles for both plwMND and carers of plwMND were clinically meaningful and representative of the people identified in practice. This will be examined formally during the next stage of the project where vignettes for each cluster profile were written and presented in workshops to clinicians, plwMND and their carers.
Limitations
Firstly, there is an over-representation of people in this study who have worked in professional occupations with higher levels of education. Australian data from 2020 [34], indicate that 35% of Australians have no qualifications after finishing secondary school. This is substantially more than the 21.9% we observed in our study [34]. Despite the discrepancy, our results and cluster profiles reveal relatable patterns of health literacy characteristics and several significant challenges for the MND community and health care providers to address [30].
Secondly, the study recruited mostly participants from Victoria where the study was based, (approximately a ratio of 3:1, Victorian to non-Victorian participants). The impact of this geographical bias in recruitment may be important as models of care vary across the Australian states, although all citizens do have access to universal health coverage.
Thirdly, the number of recruited participants may be considered a limitation, particularly, in the carers’ arm. Recruitment was challenging due to the COVID-19 pandemic, with restricted face-to-face appointments and exposure to the usual means of advertisement with posters/brochures visible in clinic. We also recognise that participation in the study was time consuming and some participants were unable to provide complete data. The survey took approximately 15 to 30 min to complete. Nonetheless, plwMND already have many challenges and it is important to minimise the burden of research on them. Consistent with previous Ophelia studies, 228 participants was ample to complete a robust cluster analysis [25, 35, 36] As described in the Ophelia manual [14] and highlighted again by Hawkins et al., “many Ophelia studies indicate that sample sizes of over 100 will generate rich information about potential subpopulations of people” [37]. A larger sample size would have enabled clearer definition of the smaller clusters in the community.
Conclusion
This is the first study to explore systematically the health literacy strengths and challenges of plwMND and their carers. The key message is that those living with MND are individuals whose experience in managing this progressive neurological disease is associated with and influenced by many factors, both intrinsic to their own lives and associated with the wider environment of the health care system. Health care providers and the way health information is delivered should attend and respond to this diversity—building on social and cultural strengths within families and communities, understanding why some people can be excluded by inflexible systems. Without this, the translation of best practice in MND care is more likely to fail, depriving individuals of the knowledge and tools to self-manage their disease and make preferred choices. Optimising health literacy for plwMND will require tailored and adaptive approaches, with a particular focus on how e-health care and information need to improve. The next steps in this project will engage with stakeholders to identify, prioritise and co-design improvements in the health care journey for all those affected by MND.
Supplementary Information
Acknowledgements
We wish to thank all participants who contributed to completing the online survey. Our hope is this body of work leads to improvements to deliver better care for those living with MND and their closest supports.
Abbreviations
- PlwMND
People living with Motor Neurone Disease
- MND
Motor Neurone Disease
- ALS
Amyotrophic Lateral Sclerosis
- DASS-21
Depression, Anxiety and Stress Scale
Authors’ contributions
AD was the main author of the paper and contributed to the analysis of the data. MC and JS were involved in conceptualisation and editing. AH and LZ were involved in database development, management and initial analysis. CC performed the hierarchical cluster analysis for the cluster profiles, wrote the methodology of the manuscript and edited the manuscript. SM, FF and MMc were involved in conceptualisation, design, delivery of the survey and data analysis. SM and RHO were major contributors to the design, analysis, direction and writing of the manuscript. All authors were involved in reviewing and editing the manuscript. All authors read and approved the final manuscript.
Funding
This project was supported by an Innovator grant from MND Research Australia, (Superball XV1 MND Research Grant). RHO was funded in part through a National Health and Medical Research Council of Australia Principal Research Fellowship #APP1155125 and Investigator Grant #APP2025522.
Data availability
The datasets generated and analysed are not publicly available as they contain potentially identifiable demographic data which may expose participants. Further information on the published data may be made available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Ethics approval was obtained through the Research Ethics and Ethics Committee at Calvary Health Care Bethlehem (approval number 20061803). Informed consent was obtained from all participants. Participant informed consent, participant privacy and confidentiality and data sharing between collaborators upheld the ethical principles of the Declaration of Helsinki developed by the World Medical Association.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and analysed are not publicly available as they contain potentially identifiable demographic data which may expose participants. Further information on the published data may be made available from the corresponding author upon request.