Healthy cognitive ageing: Empowering older adults through self-testing
Lead Research Organisation:
University of Leicester
Department Name: Neuroscience, Psychology and Behaviour
Abstract
From around age 50, people experience increasing problems with thinking ("cognition"), and 1 in 5 go on to develop diagnosed cognitive impairment. In our ageing population, rising numbers of older people are experiencing worsening problems with memory, concentration, and multitasking. Without support, this increasingly affects day-to-day life. As average retirement age rises, it also prevents older adults from maintaining skilled work. This affects individuals' activity and wellbeing, and employers lose critical skills and experience from their workforce.
Accordingly, identifying these cognitive problems in older adults is crucial to helping them access support, continue working, and fully enjoy life. However, NHS resources cannot assess all older adults for potential cognitive changes. Here, we outline a new approach to identifying cognitive problems, while minimising reliance on stretched healthcare resources.
We will develop an online-access cognitive test for older adults, which can be self-administered at home to test and track cognitive changes. The test will use simple computer-based tasks which are already individually proven to detect changes in memory, concentration, and multi-tasking. This will allow older adults to independently self-test their cognition, as people with diabetes check their blood sugar, without attending specialist clinics.
The test will produce results meaningful to the older person, healthcare professionals, and workplace support systems like occupational health. It will provide results-based recommendations, such as signposting to support services, or using practical, brain-training and social strategies to help manage cognitive changes. We aim to empower older adults to monitor their own cognitive wellbeing, and to identify those needing support to reduce avoidable impact on their wellbeing and work.
Accordingly, identifying these cognitive problems in older adults is crucial to helping them access support, continue working, and fully enjoy life. However, NHS resources cannot assess all older adults for potential cognitive changes. Here, we outline a new approach to identifying cognitive problems, while minimising reliance on stretched healthcare resources.
We will develop an online-access cognitive test for older adults, which can be self-administered at home to test and track cognitive changes. The test will use simple computer-based tasks which are already individually proven to detect changes in memory, concentration, and multi-tasking. This will allow older adults to independently self-test their cognition, as people with diabetes check their blood sugar, without attending specialist clinics.
The test will produce results meaningful to the older person, healthcare professionals, and workplace support systems like occupational health. It will provide results-based recommendations, such as signposting to support services, or using practical, brain-training and social strategies to help manage cognitive changes. We aim to empower older adults to monitor their own cognitive wellbeing, and to identify those needing support to reduce avoidable impact on their wellbeing and work.
Organisations
Publications
Cosgriff E
(2023)
Effects of Type 2 Diabetes on attention in middle-aged and older adults
| Description | In this project, we aimed to explore whether it is feasible and achievable to use a home-based measure of cognitive function (thinking skills) to evaluate risk of cognitive difficulties in older adults. We were able to successfully design and implement a working version of our test, identifying an effective measure which evaluates key early changes in cognition. The test has previously only been used for in-person assessments or under the supervision of a researcher to our knowledge, and our findings demonstrated that it can be applied robustly by older adults who are completing a self-assessment at home on their own computer. This is very exciting, as it opens up the possibility of applying a simple diagnostic tool at home to identify early cognitive difficulties. Importantly, we were also able to evaluate how we can best deliver this test. Early versions were hard for users to understand - on one occasion almost a third of users misunderstood the task. This reinforced the importance of the iterative approach we took to design. The online platform we used enabled easy user experience feedback, and we were able to repeatedly revise the test process to clarify the design and make it more accessible, until we had a good working version. We are currently evaluating the outcomes from our datasets. We have gathered enough good-quality data to support an initial investigation into how well our test can discriminate between age brackets in people who are "typically ageing" (i.e., who do not have dementia or another diagnosis of a cognitive difficulty). Our next step is to gather a larger amount of data to develop "norms" for the test, which would enable it to be used diagnostically by comparing a person's test results to where we might expect them to be, compared to their peers. There are additional important outcomes from our project. We know that certain medical conditions can impact on cognitive function, so we also collected data in relation to key conditions when people completed the test. One key example is that we collated information in relation to type II diabetes, finding significant differences in cognitive function for people with this diagnosis. This work has been presented at a conference and will be written up as an academic paper. In terms of next steps, we have further research questions to address from our dataset around the impact of lifestyle and demographics on cognitive ageing, which are targets for future work. The project has also underpinned the lead researcher taking an advisory role on a social enterprise organisation targeting support for caregivers of older adults with dementia. This provides a future route for dissemination and promotion of the test and outcomes. Further discussion of next steps is reported below. |
| Exploitation Route | We are currently writing up two publications around the datasets noted above, focusing on the applicability of home-based self-testing of cognition for older adults, and the impacts of cognitive ageing associated with type II diabetes. Further papers will focus on impacts of lifestyle and demographics on early cognitive ageing. Our next step is to gather substantially more data to develop test norms, to enable more systematic evaluation of the test's diagnostic utility. We are undertaking this through multiple routes. First, three doctoral students are conducting thesis work using either the test we used or related versions, to identify whether our test is effective in different ageing populations and contexts, or whether additional components might improve its effectiveness. In terms of seeking additional funding, we have developed initial relationships with NHS partners and scoped interest from charity partners, with very positive responses. We will therefore be seeking additional funding for this work, once we have completed initial analyses of the data and published to demonstrate the robustness of our findings. |
| Sectors | Healthcare |
| Title | Cognitive Change in Healthy Ageing: Study data set 1 |
| Description | Anonymous data for experimental work to pilot a measure of cognitive change in ageing and gather data about cognitive problems experienced by typically-ageing adults, as part of exploratory work towards producing a diagnostic product for older adults experiencing cognitive change. Data were gathered through an online behavioural task platform (Gorilla) using cognitive tasks and standardised questionnaires (PHQ9, GAI, ESS, NAVQ, GPAQ) plus some non-standardised questions about the impact of perceived cognitive change. Data files are included for all components. - Demographic data, including: equipment used to complete test; age; gender; ethnicity; work status; level of education; accommodation; marital or partnership status; people in household; reported health conditions; use of alcohol/cigarettes/non-prescribed drugs. - Task data - user experience; priorities for task development - Psychometric data - standardised questionnaire information: Patient Health Questionnaire 9; Geriatric Anxiety Inventory; Epworth Sleepiness Scale; Near Activity Visual Questionnaire; Global Physical Activity Questionnaire; non-standardised questions asking about experience of cognitive change in ageing - Cognitive task data - accuracy and reaction times to visual stimuli |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | We are currently working towards publications from this dataset. |
| URL | https://figshare.le.ac.uk/articles/dataset/Cognitive_Change_in_Healthy_Ageing_Study_data_set_1/24659... |
| Title | Cognitive Change in Healthy Ageing: Study data set 2 |
| Description | Anonymous data for experimental work to pilot a measure of cognitive change in ageing and gather data about cognitive problems experienced by typically-ageing adults, as part of exploratory work towards producing a diagnostic product for older adults experiencing cognitive change. Data were gathered through an online behavioural task platform (Gorilla) using cognitive tasks and standardised questionnaires (PHQ9, GAI, ESS, NAVQ, GPAQ) plus some non-standardised questions about the impact of perceived cognitive change. In this second experiment, we also collected information about diabetes status alongside ageing data, as this was anticipated to potentially impact on cognitive ageing in an interaction with age. Data files are included for all components. - Demographic data, including: equipment used to complete test; age; gender; ethnicity; work status; level of education; accommodation; marital or partnership status; people in household; reported health conditions; use of alcohol/cigarettes/non-prescribed drugs; diabetes status - Psychometric data - standardised questionnaire information: Patient Health Questionnaire 9; Epworth Sleepiness Scale; Near Activity Visual Questionnaire; Global Physical Activity Questionnaire; Problem Areas in Diabetes Questionnaire - Cognitive task data - accuracy and reaction times to visual stimuli including cue type and response |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | We are working towards publications from this dataset. |
| URL | https://figshare.le.ac.uk/articles/dataset/Cognitive_Change_in_Healthy_Ageing_Study_data_set_2/24659... |
| Description | Presentation at "Celebrating Innovation in Healthy Ageing" engagement day |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | Engagement event in which people developing businesses to promote healthy ageing outlined their work in an accessible manner to an audience of practitioners, businesspeople, healthcare professionals, third sector representatives and laypeople with an interest in ageing. There was active discussion and questions after presentations and informally afterwards, and in particular I developed a link with the head of an online platform for supporting healthy ageing. She has subsequently recruited me as an advisor for her organisation, based on my academic and clinical knowledge. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://eldering.co.uk/our-experts/ |